1 00:00:05,053 --> 00:00:07,202 A down beef cap, is she gonna make 2 00:00:07,202 --> 00:00:08,236 it or not. 3 00:00:09,271 --> 00:00:11,365 That's question will explore as we look at 4 00:00:11,365 --> 00:00:14,564 a research paper that ask about what impacts 5 00:00:14,564 --> 00:00:17,364 whether those cows do well after being down. 6 00:00:18,178 --> 00:00:20,570 Welcome. It's after the abstract. I'm Brad White. 7 00:00:20,809 --> 00:00:23,121 Joined today by doctor Brian Lube. Good morning, 8 00:00:23,280 --> 00:00:26,204 Brian. Morning, Brad. Brian, this is a great 9 00:00:26,642 --> 00:00:29,270 1 of our Bo Science with Bc podcast 10 00:00:29,270 --> 00:00:30,623 podcasts as we get a chance to go 11 00:00:30,623 --> 00:00:32,772 through some of the papers that come out 12 00:00:32,772 --> 00:00:34,046 in the literature and hopefully, 13 00:00:34,698 --> 00:00:36,130 go through them in a way that will 14 00:00:36,130 --> 00:00:38,358 provide some helpful information to you. And then 15 00:00:38,358 --> 00:00:39,869 if you want, you can go look up 16 00:00:39,869 --> 00:00:40,347 the papers. 17 00:00:40,904 --> 00:00:42,575 As we go through these papers. If you 18 00:00:42,575 --> 00:00:44,825 ever have a paper, or a topic that's 19 00:00:44,825 --> 00:00:46,585 of interest to you that you'd like us 20 00:00:46,585 --> 00:00:48,265 to talk about and read through. You can 21 00:00:48,265 --> 00:00:50,265 send those to us at bc at KSU 22 00:00:50,265 --> 00:00:50,825 dot edu. 23 00:00:51,399 --> 00:00:53,237 But you picked a good paper for today. 24 00:00:53,397 --> 00:00:54,595 It tell us about our paper today. Brian? 25 00:00:54,915 --> 00:00:57,072 It's kind of an interesting topic. So the 26 00:00:57,072 --> 00:00:58,131 title is pro 27 00:00:58,510 --> 00:01:01,002 indicators about com in non ambulatory beef cattle, 28 00:01:01,642 --> 00:01:04,439 presented to 2 referral hospitals, a retrospective study 29 00:01:04,439 --> 00:01:07,235 of 63 cases, and it was published in 30 00:01:07,475 --> 00:01:09,483 April of 20. So it's a recent article 31 00:01:09,483 --> 00:01:11,951 in the journal of internal veterinary medicine. Yeah. 32 00:01:12,189 --> 00:01:13,884 And the cool part is this is 33 00:01:14,578 --> 00:01:17,219 between Auburn and K 4 state and and 34 00:01:17,219 --> 00:01:19,765 some good faculty at both both institutions put 35 00:01:19,765 --> 00:01:22,628 this together. And and really, they ask a 36 00:01:22,628 --> 00:01:23,128 good 37 00:01:23,679 --> 00:01:25,514 research question, which is where we start and 38 00:01:25,514 --> 00:01:27,110 just to review, as we as we go 39 00:01:27,110 --> 00:01:28,547 through and assess these papers, 40 00:01:29,664 --> 00:01:31,715 we always start with the abstract and isn't 41 00:01:31,834 --> 00:01:33,429 relevant to our clinical scenario. 42 00:01:33,908 --> 00:01:35,663 Then we're gonna look at results, and then 43 00:01:35,663 --> 00:01:36,939 we're gonna look at how they did it. 44 00:01:37,098 --> 00:01:38,135 The materials and methods, 45 00:01:38,933 --> 00:01:40,687 as well as how they drew some of 46 00:01:40,687 --> 00:01:43,483 their conclusions and then finally, we'll we'll wrap 47 00:01:43,483 --> 00:01:45,941 up with kinda of reviewing what what we 48 00:01:45,941 --> 00:01:47,764 learned. So I wanna start there with the 49 00:01:47,764 --> 00:01:49,112 abstract, Ryan. And when you look at it, 50 00:01:49,350 --> 00:01:49,405 and 51 00:01:50,242 --> 00:01:51,917 what do we see here that is it 52 00:01:51,917 --> 00:01:54,629 relevant to us clinically or not? Yeah. And 53 00:01:54,709 --> 00:01:55,267 I think so, 54 00:01:56,224 --> 00:01:58,718 you know, and they kinda the the abstract 55 00:01:58,776 --> 00:01:59,276 for 56 00:02:00,146 --> 00:02:01,921 the journal of veterinary internal medicine 57 00:02:02,696 --> 00:02:04,528 is broken down by section. And so they, 58 00:02:04,608 --> 00:02:06,361 you know, they say the in the background, 59 00:02:06,854 --> 00:02:08,762 You know, we know a little bit about 60 00:02:08,762 --> 00:02:11,784 down syndrome and dairy cattle, but there's kind 61 00:02:11,784 --> 00:02:13,772 of a lack of information and beef cattle. 62 00:02:13,931 --> 00:02:15,919 And so I think I think it's actually 63 00:02:15,919 --> 00:02:16,578 a really 64 00:02:16,968 --> 00:02:18,877 very pertinent to what we're talking about. And 65 00:02:18,877 --> 00:02:20,707 they're... In what we'd like to do here 66 00:02:20,707 --> 00:02:21,582 in this podcast. 67 00:02:22,138 --> 00:02:24,856 And their objective is clearly stated, they say 68 00:02:24,856 --> 00:02:27,637 they evaluate the records of beef cattle, greater 69 00:02:27,637 --> 00:02:29,146 than or equal to 2 years of age 70 00:02:29,146 --> 00:02:30,656 presented at 2 referral hospitals. 71 00:02:31,148 --> 00:02:32,603 To identify pro 72 00:02:33,058 --> 00:02:35,844 indicators for survival to discharge. So, yeah. I 73 00:02:35,844 --> 00:02:39,284 think that's pretty relevant to most practitioners our 74 00:02:39,284 --> 00:02:41,364 practitioner audience anyway. Well, that's a come it's 75 00:02:41,364 --> 00:02:43,604 a common thing. You get down cows in 76 00:02:43,604 --> 00:02:44,564 in beef practice. 77 00:02:44,965 --> 00:02:46,564 And the question is, are they gonna make 78 00:02:46,564 --> 00:02:47,924 it or not? And and a lot of 79 00:02:47,924 --> 00:02:50,725 times We just don't know the answer. So 80 00:02:50,725 --> 00:02:52,554 what what is some of their kind of 81 00:02:52,554 --> 00:02:55,973 major conclusions here as we decide, will it 82 00:02:55,973 --> 00:02:58,374 influence the way I practice or or What 83 00:02:58,374 --> 00:03:00,368 are some of the big things? Yeah. I 84 00:03:00,368 --> 00:03:00,448 think, 85 00:03:01,405 --> 00:03:03,799 probably the first big thing. And and again, 86 00:03:03,958 --> 00:03:04,995 we're kinda digging... 87 00:03:05,489 --> 00:03:07,484 Towards the bottom here, but, you know, if 88 00:03:07,484 --> 00:03:08,441 you read through this, 89 00:03:09,478 --> 00:03:11,952 it's pretty clear that beef cattle are not 90 00:03:11,952 --> 00:03:15,245 dairy cattle. And so... Because because that's a 91 00:03:15,245 --> 00:03:17,085 good conclusion. Yeah. Well, that's not what they. 92 00:03:17,245 --> 00:03:20,125 That's my interpretation. That's your own own interpretation. 93 00:03:20,284 --> 00:03:22,939 Yeah. Because what they found... So again, this 94 00:03:22,939 --> 00:03:26,159 is a a retrospective analysis of cases 95 00:03:26,539 --> 00:03:28,379 that presented to our referral hospital, 96 00:03:28,699 --> 00:03:30,400 and they had 63 97 00:03:30,794 --> 00:03:33,194 but only 12 of those survived. So they 98 00:03:33,194 --> 00:03:35,275 were right right around 20 percent of the 99 00:03:35,275 --> 00:03:35,775 cases 100 00:03:36,314 --> 00:03:39,050 survived until discharged for beef cattle, But then 101 00:03:39,050 --> 00:03:41,689 they quote, you know, in the literature, for 102 00:03:41,689 --> 00:03:45,289 dairy cattle, the report the reported survival to 103 00:03:45,289 --> 00:03:47,449 discharge ranges from, like, 30 to 50 percent. 104 00:03:47,689 --> 00:03:50,507 So I think the 1 of the major 105 00:03:50,562 --> 00:03:52,624 takeaways from this is the... As you said, 106 00:03:52,782 --> 00:03:54,923 the prognosis is bad. If you have a 107 00:03:54,923 --> 00:03:55,478 downer cow, 108 00:03:56,369 --> 00:03:57,967 If it's a dairy cow. It's bad. If 109 00:03:57,967 --> 00:03:59,885 it's a beef cow that presents like this. 110 00:04:00,045 --> 00:04:02,603 It's it's really bad. So that... That's that 111 00:04:02,603 --> 00:04:04,201 for me was a kind of a big 112 00:04:04,201 --> 00:04:05,560 take home. Now, 113 00:04:06,213 --> 00:04:08,600 with keeping that in mind, you know, 1 114 00:04:08,600 --> 00:04:11,249 of their their objective is really, could they 115 00:04:11,545 --> 00:04:12,102 identify things, 116 00:04:12,738 --> 00:04:13,238 factors, 117 00:04:13,853 --> 00:04:14,830 animal characteristics, 118 00:04:15,699 --> 00:04:17,560 that would help pro 119 00:04:18,500 --> 00:04:20,660 that. And so you could make the the 120 00:04:20,660 --> 00:04:23,139 clinical decision earlier that this is a case 121 00:04:23,139 --> 00:04:25,194 that we should keep moving forward with or 122 00:04:25,394 --> 00:04:27,148 this is a case where we we probably 123 00:04:27,148 --> 00:04:29,540 just the animal because a prognosis is not 124 00:04:29,540 --> 00:04:32,331 good. So so that's that's really what they 125 00:04:32,331 --> 00:04:34,817 did. And and again, keeping in mind, this 126 00:04:34,817 --> 00:04:37,593 is a retrospective study. So that... It's a 127 00:04:37,593 --> 00:04:39,577 different type of study. Right? So it's... 128 00:04:40,450 --> 00:04:42,354 You use those studies for... 129 00:04:43,323 --> 00:04:44,380 Diseases where 130 00:04:44,836 --> 00:04:47,305 maybe they're not... There's not a high prevalence, 131 00:04:47,544 --> 00:04:49,318 so, you know, they've looked 132 00:04:50,013 --> 00:04:52,918 10 years, 12 years worth of hospital records 133 00:04:53,216 --> 00:04:55,370 in 2 institutions and they came up with 134 00:04:55,370 --> 00:04:58,343 63 cases that met their inclusion criteria. So 135 00:04:58,482 --> 00:04:59,254 not many 136 00:04:59,771 --> 00:05:02,076 and certainly, this isn't a a type of 137 00:05:02,076 --> 00:05:02,950 disease where we could, 138 00:05:03,903 --> 00:05:06,049 go out and we either do an induced 139 00:05:06,049 --> 00:05:08,059 model or probably find enough cases 140 00:05:08,448 --> 00:05:11,227 to to make some conclusions. So that's why 141 00:05:11,227 --> 00:05:12,974 you would do a retrospective. You can take 142 00:05:12,974 --> 00:05:14,880 a long look back and try to pool 143 00:05:14,880 --> 00:05:17,634 enough cases to to at least get some 144 00:05:18,151 --> 00:05:20,691 either form some more hypotheses or get some 145 00:05:20,691 --> 00:05:23,867 preliminary findings on what are those pro factors. 146 00:05:24,105 --> 00:05:25,693 So they looked at a lot of things. 147 00:05:25,931 --> 00:05:26,779 They looked at 148 00:05:27,298 --> 00:05:28,277 animal signal. 149 00:05:28,655 --> 00:05:32,187 They looked at some blood chemistry values. 150 00:05:33,362 --> 00:05:37,551 They looked at different management, hospital management strategies. 151 00:05:38,170 --> 00:05:40,327 And 1 of the other big conclusions, and 152 00:05:40,486 --> 00:05:42,659 I was a little bit surprised, most of 153 00:05:42,659 --> 00:05:45,536 those things weren't p... Pro. So some of 154 00:05:45,536 --> 00:05:47,613 the things that I would have anticipated would 155 00:05:47,613 --> 00:05:51,050 have been pro would be non steroid treatment. 156 00:05:51,384 --> 00:05:52,661 Right? I I would've have thought, you know, 157 00:05:53,140 --> 00:05:54,598 non oils or cor. 158 00:05:55,136 --> 00:05:57,052 You know, I thought, well, those probably have 159 00:05:57,052 --> 00:05:59,925 a positive impact on case outcome. And and 160 00:05:59,925 --> 00:06:00,300 they're 161 00:06:00,738 --> 00:06:02,812 study they did not. I was kinda surprised 162 00:06:02,812 --> 00:06:05,843 even more simply that there there's pretty good 163 00:06:05,843 --> 00:06:07,699 split on which ones got cor 164 00:06:08,076 --> 00:06:09,671 or not, which a lot of times in 165 00:06:09,671 --> 00:06:11,602 a downer out. Oh, I'm like, yeah, yeah. 166 00:06:11,761 --> 00:06:13,437 You He's she's probably gonna get some. Yeah. 167 00:06:13,597 --> 00:06:14,076 So Yeah. 168 00:06:14,954 --> 00:06:16,710 But that also probably speaks to... 169 00:06:17,428 --> 00:06:19,759 They included a variety of diagnoses. And and 170 00:06:19,759 --> 00:06:22,256 in fact, across their their 63 171 00:06:22,316 --> 00:06:22,795 animals, 172 00:06:23,354 --> 00:06:24,633 there were... And this is 173 00:06:25,512 --> 00:06:27,683 retrospective case study. So you get this, There 174 00:06:27,683 --> 00:06:30,307 were several that were not defined. Right? And 175 00:06:30,307 --> 00:06:32,795 you had everything that range from true mu 176 00:06:32,852 --> 00:06:33,829 skeletal to 177 00:06:34,283 --> 00:06:35,476 maybe lymph sarcoma, 178 00:06:35,968 --> 00:06:37,262 Gi, polio, 179 00:06:37,874 --> 00:06:40,257 hardware. I mean, the whole gamut. 180 00:06:40,733 --> 00:06:42,481 So you can see why some of those 181 00:06:42,481 --> 00:06:45,284 would be infectious diseases and might not get 182 00:06:46,232 --> 00:06:48,944 a cor stair. Yeah. That that that makes 183 00:06:48,944 --> 00:06:50,938 sense to me once I've really kinda thought 184 00:06:50,938 --> 00:06:53,331 it through. Yeah. And, you know, 1... You 185 00:06:53,331 --> 00:06:55,165 know, we're... You're asking about big finding is 186 00:06:55,165 --> 00:06:57,561 in conclusions. 1 of the things that was 187 00:06:57,561 --> 00:07:00,814 associated with a positive prognosis was the diagnosis. 188 00:07:00,973 --> 00:07:01,632 And so 189 00:07:02,401 --> 00:07:05,621 beef cattle that had that came in with 190 00:07:05,759 --> 00:07:09,529 a diagnosis of having peripheral nerve paralysis, 191 00:07:10,624 --> 00:07:13,118 they they actually had a pretty good chance 192 00:07:13,176 --> 00:07:13,676 of 193 00:07:14,068 --> 00:07:17,094 an increased chance of survival to discharge. And 194 00:07:17,094 --> 00:07:19,165 and pretty good in this population 50 50. 195 00:07:19,404 --> 00:07:20,200 Yeah. Yes. 196 00:07:20,917 --> 00:07:23,159 So better better than the other diet. Right? 197 00:07:23,318 --> 00:07:25,460 But the other diagnoses like you said, includes 198 00:07:25,460 --> 00:07:27,206 things life. This lymph sarcoma. 199 00:07:28,158 --> 00:07:30,797 Sounds like it includes things like, major limb 200 00:07:30,797 --> 00:07:33,189 fractures, so all of that's included. So, you 201 00:07:33,189 --> 00:07:34,545 know, like I said, as you read through 202 00:07:34,545 --> 00:07:36,140 it and you kind think about it, a 203 00:07:36,140 --> 00:07:37,895 lot of these things do make sense, But 204 00:07:37,895 --> 00:07:39,785 it... It's nice to have somebody that acts 205 00:07:40,064 --> 00:07:41,659 looked at it. But I think I think 206 00:07:41,659 --> 00:07:43,334 that's and I'll I'll just as as we're 207 00:07:43,334 --> 00:07:45,089 going through results, and this is from table 208 00:07:45,089 --> 00:07:48,997 2. So the 8 out of 18 were 209 00:07:48,997 --> 00:07:49,316 discharged. 210 00:07:50,288 --> 00:07:53,402 Mu skeletal spinal cord disease, only 1 out 211 00:07:53,402 --> 00:07:54,998 of 17 was discharged, 212 00:07:55,557 --> 00:07:58,431 and the other category, only 3 out of 213 00:07:58,431 --> 00:07:59,070 25. 214 00:07:59,323 --> 00:08:01,705 Were discharged. So you're you're right. I mean, 215 00:08:01,784 --> 00:08:03,951 a 50 50 shot compared to 216 00:08:04,325 --> 00:08:05,357 1 out of 17, 217 00:08:05,833 --> 00:08:07,262 not super good at math, but I'd say 218 00:08:07,262 --> 00:08:09,586 that's a lot better. Yeah. And I think 219 00:08:09,586 --> 00:08:11,899 too, keep in mind with these numbers. The... 220 00:08:12,138 --> 00:08:13,893 This is a different topic. Right. These are 221 00:08:13,893 --> 00:08:16,046 animals that are presenting to a teaching hospital. 222 00:08:16,206 --> 00:08:18,613 So there's already been some case select action. 223 00:08:19,011 --> 00:08:21,001 So I don't know if that makes the 224 00:08:21,001 --> 00:08:21,501 odds 225 00:08:22,037 --> 00:08:24,346 better or worse. Right? Because we've probably already... 226 00:08:24,584 --> 00:08:26,677 A lot of animals that had major limb 227 00:08:26,908 --> 00:08:27,965 fractures were probably 228 00:08:28,340 --> 00:08:30,328 on farm. It never would have come into 229 00:08:30,328 --> 00:08:32,635 this. They wouldn't never be in this retrospective 230 00:08:32,635 --> 00:08:34,942 study. So... But, yeah, You're right. I mean, 231 00:08:35,673 --> 00:08:38,457 keep kinda keep in mind, it's... The... Those 232 00:08:38,457 --> 00:08:40,548 factors are statistically associated. 233 00:08:41,003 --> 00:08:43,165 But if you look at this paper, really 234 00:08:43,165 --> 00:08:45,150 paying attention to those raw numbers, I think 235 00:08:45,150 --> 00:08:47,134 is is important because it's still... Like you 236 00:08:47,134 --> 00:08:48,960 said, it's still a coin flip for even 237 00:08:48,960 --> 00:08:50,151 for ga paralysis. 238 00:08:50,643 --> 00:08:53,109 Yeah. I think that's absolutely right. So as 239 00:08:53,109 --> 00:08:55,017 we look through those tables and and they've 240 00:08:55,017 --> 00:08:57,722 got a a breakdown of the different diseases, 241 00:08:57,881 --> 00:08:58,699 different diagnoses. 242 00:08:59,327 --> 00:09:01,494 They've got a table that that highlights 243 00:09:01,867 --> 00:09:02,843 some of the 244 00:09:03,217 --> 00:09:03,717 uni 245 00:09:04,169 --> 00:09:05,995 analyses, and then they put them together in 246 00:09:05,995 --> 00:09:08,558 a multi... Very process. And I think that's 247 00:09:08,558 --> 00:09:10,812 maybe worth spending just a second on 248 00:09:11,189 --> 00:09:13,900 is the the typical process when we have 249 00:09:13,900 --> 00:09:16,067 a lot of variables that we wanna weight 250 00:09:16,067 --> 00:09:18,001 in 1 of these retrospective analyses 251 00:09:18,457 --> 00:09:20,926 is to look at each 1 of them 252 00:09:20,926 --> 00:09:23,236 individually? Are they associated with our outcome of 253 00:09:23,236 --> 00:09:25,642 discharge or not discharge. And then they put 254 00:09:25,642 --> 00:09:27,579 them all together in a 255 00:09:27,956 --> 00:09:31,148 single model which tells us okay, what were 256 00:09:31,148 --> 00:09:33,155 kinda some of the major effects. And and 257 00:09:33,155 --> 00:09:35,060 when they did that, only a few things 258 00:09:35,060 --> 00:09:36,965 came up as significance. Is that right? Right? 259 00:09:37,282 --> 00:09:40,536 Yeah. So in the the uni area analysis, 260 00:09:40,774 --> 00:09:42,543 which is that first step that you talked 261 00:09:42,543 --> 00:09:44,321 about where we're looking at each 262 00:09:44,700 --> 00:09:45,200 variable 263 00:09:45,579 --> 00:09:46,079 independently, 264 00:09:47,097 --> 00:09:47,836 the age 265 00:09:48,375 --> 00:09:48,830 was 266 00:09:49,268 --> 00:09:49,768 statistically 267 00:09:50,144 --> 00:09:52,135 significant. And as you would expect, 268 00:09:52,773 --> 00:09:55,185 younger animals had a better prognosis. 269 00:09:55,959 --> 00:09:57,871 And 1 more thing to to think about 270 00:09:57,871 --> 00:09:58,245 is 271 00:09:58,760 --> 00:10:00,451 some... A lot of these things 272 00:10:01,141 --> 00:10:02,038 are also 273 00:10:02,491 --> 00:10:05,213 confounded with, you know, people are making clinical 274 00:10:05,427 --> 00:10:07,982 decisions as they went through these cases. Right? 275 00:10:08,141 --> 00:10:09,331 And so, you know, 276 00:10:10,045 --> 00:10:12,346 some of that is... Well, if I have 277 00:10:12,346 --> 00:10:14,275 an older animal, I'm probably 278 00:10:14,824 --> 00:10:16,681 less likely to put 279 00:10:17,139 --> 00:10:18,038 invest more 280 00:10:18,495 --> 00:10:20,810 resources or case management into that apple. And 281 00:10:20,810 --> 00:10:22,566 the authors talk about that in the discussion 282 00:10:22,566 --> 00:10:24,335 this paper, but you know, keep in mind 283 00:10:24,335 --> 00:10:27,384 that this isn't this isn't necessarily a static 284 00:10:27,601 --> 00:10:30,389 decision. We're we're making these decisions, like, they 285 00:10:30,389 --> 00:10:32,062 all come together. She's an older animal. 286 00:10:33,434 --> 00:10:36,174 Probably not as likely to be discharged simply 287 00:10:36,235 --> 00:10:38,715 because I'm not gonna invest the same resources. 288 00:10:38,875 --> 00:10:41,914 But age was significantly associated with outcomes. 289 00:10:42,649 --> 00:10:44,987 Length of hospital stay was significantly 290 00:10:45,685 --> 00:10:48,641 associated with outcomes, and and as you would 291 00:10:48,641 --> 00:10:49,620 expect the longer 292 00:10:50,093 --> 00:10:52,798 They stayed in the hospital, the more likely 293 00:10:52,798 --> 00:10:54,070 they were to be discharged. 294 00:10:55,343 --> 00:10:56,559 The number 295 00:10:57,028 --> 00:10:59,598 I'm sorry. The... Yeah. The number of flotation 296 00:10:59,655 --> 00:11:00,155 sessions 297 00:11:00,849 --> 00:11:05,169 was significantly associated with discharge, although the total 298 00:11:05,227 --> 00:11:06,261 time was not, 299 00:11:06,992 --> 00:11:08,207 weight was not 300 00:11:08,581 --> 00:11:11,441 associated with the prognosis. And I I would 301 00:11:11,441 --> 00:11:13,450 have expected that lighter animals 302 00:11:13,998 --> 00:11:16,940 would have done had a better prognosis simply 303 00:11:16,940 --> 00:11:19,427 because we don't have the same 304 00:11:19,801 --> 00:11:21,471 sequel. Like, if you have a down cow, 305 00:11:21,630 --> 00:11:23,553 and you have a big down cow, There 306 00:11:23,553 --> 00:11:25,380 are some other Sequel sequoia that happened that 307 00:11:25,539 --> 00:11:27,287 I think would have had a negative impact 308 00:11:27,287 --> 00:11:28,025 on prognosis. 309 00:11:28,716 --> 00:11:30,408 Some of that may have just been 310 00:11:30,798 --> 00:11:32,389 I mean, they did have a pretty wide 311 00:11:32,389 --> 00:11:34,616 range of weights in both of these categories. 312 00:11:34,776 --> 00:11:37,162 So... It's interesting too as you go through 313 00:11:37,162 --> 00:11:39,152 the process, weight a little bit surprising. 314 00:11:39,724 --> 00:11:42,218 Aids not surprising, but when they put everything 315 00:11:42,356 --> 00:11:44,429 together in their final model, 316 00:11:45,067 --> 00:11:46,822 it comes up flotation therapy. 317 00:11:47,779 --> 00:11:49,077 Duration of hospital stay 318 00:11:49,468 --> 00:11:51,555 pregnancy status at presentation 319 00:11:52,086 --> 00:11:52,586 diagnosis 320 00:11:53,594 --> 00:11:55,418 were were the only things that came up. 321 00:11:56,053 --> 00:11:57,505 Here's where I think 322 00:11:58,294 --> 00:12:00,051 we need to maybe distinguish this from some 323 00:12:00,051 --> 00:12:01,489 of the other papers that we talk about. 324 00:12:02,048 --> 00:12:03,806 When we... After we review the results and 325 00:12:03,806 --> 00:12:06,451 the tables, we we go and talk about 326 00:12:06,451 --> 00:12:07,962 appropriate control for bias. 327 00:12:08,439 --> 00:12:10,268 Now, in this study, and we do a 328 00:12:10,268 --> 00:12:12,654 lot of retrospective studies here as well, 329 00:12:13,543 --> 00:12:16,408 my control for bias is, I can't. Right. 330 00:12:16,727 --> 00:12:19,910 There's there's bias embedded in the data, and 331 00:12:19,910 --> 00:12:22,059 the only way that I can manage from 332 00:12:22,059 --> 00:12:25,115 a retrospective study, and they are really good 333 00:12:25,488 --> 00:12:26,464 for hypothesis 334 00:12:26,916 --> 00:12:30,669 generation. They're not great at sorting out c. 335 00:12:31,455 --> 00:12:33,535 And maybe explain that a little bit, Brian 336 00:12:33,535 --> 00:12:35,695 of what what kind of conclusions should I 337 00:12:35,695 --> 00:12:38,035 make from not necessarily just this 1, but 338 00:12:38,095 --> 00:12:40,902 retrospective studies in general. Yeah. And like you 339 00:12:40,902 --> 00:12:43,526 said, and and again, I I really come 340 00:12:43,526 --> 00:12:46,388 in the authors here for doing this work 341 00:12:46,388 --> 00:12:49,029 because it's a question that hadn't really been 342 00:12:49,029 --> 00:12:51,657 answered the the whole beef cattle aspect. And 343 00:12:51,657 --> 00:12:53,727 and again, like I said, early in this 344 00:12:53,727 --> 00:12:54,205 podcast, 345 00:12:54,603 --> 00:12:56,457 you know, 1 of the big conclusions is 346 00:12:56,688 --> 00:12:59,543 beef cattle and dairy cattle, prognosis is not 347 00:12:59,543 --> 00:13:01,050 the same. For, at least from what's been 348 00:13:01,050 --> 00:13:03,509 published on Dairy Cattle and this specific paper 349 00:13:03,509 --> 00:13:05,589 and beef cattle. So I think, you know, 350 00:13:05,827 --> 00:13:08,394 I I look at this paper as 351 00:13:09,325 --> 00:13:10,222 it's helping 352 00:13:10,597 --> 00:13:12,559 explore some of the things that might 353 00:13:12,999 --> 00:13:15,796 help us make better clinical decisions. So... And 354 00:13:15,796 --> 00:13:17,874 and like you said, they looked at a 355 00:13:17,874 --> 00:13:20,112 lot of different factors, and and as you, 356 00:13:20,192 --> 00:13:21,550 kinda jump in ahead a little bit to 357 00:13:21,550 --> 00:13:23,563 the methods, but I mean, they had some 358 00:13:23,563 --> 00:13:24,303 pretty good 359 00:13:24,841 --> 00:13:27,798 inclusion and exclusion criteria. Right? And so the... 360 00:13:28,037 --> 00:13:29,875 I think their cases are well defined. 361 00:13:31,089 --> 00:13:33,986 1 of the challenges with retrospective 362 00:13:34,445 --> 00:13:36,064 cases like this is 363 00:13:36,443 --> 00:13:39,160 1 clinician might manage a case different than 364 00:13:39,160 --> 00:13:42,194 a other. Meaning, 1 clinician might do blood 365 00:13:42,194 --> 00:13:44,337 work and another 1 might not. Or 1 366 00:13:44,337 --> 00:13:46,481 clinician as we mentioned, might treat with a 367 00:13:46,560 --> 00:13:48,600 Cor steroid and 1 might not. And you 368 00:13:48,719 --> 00:13:50,870 can't control for those things. So essentially, 369 00:13:51,826 --> 00:13:54,615 at this stage of looking back at records 370 00:13:54,615 --> 00:13:56,230 and trying to to 371 00:13:56,606 --> 00:13:58,040 identify pro factors, 372 00:13:58,612 --> 00:14:00,358 you kinda have what you have in those 373 00:14:00,358 --> 00:14:03,137 records. And so they did... Actually lost a 374 00:14:03,137 --> 00:14:05,836 few cases because there was just incomplete data 375 00:14:05,836 --> 00:14:07,399 or missing data, and that's 376 00:14:07,839 --> 00:14:11,196 again, all retrospective studies have those limitations. And 377 00:14:11,196 --> 00:14:12,874 and... These authors do a good job in 378 00:14:12,874 --> 00:14:15,591 the discussion of kinda talking about that. But 379 00:14:15,591 --> 00:14:16,550 what you asked earlier, 380 00:14:17,123 --> 00:14:20,405 we have to be very careful about saying 381 00:14:20,859 --> 00:14:24,277 from a retrospective that this caused this. We 382 00:14:24,277 --> 00:14:26,026 we really need to focus on and, you 383 00:14:26,026 --> 00:14:28,437 know, we can say these 2 things are 384 00:14:28,437 --> 00:14:28,937 associated. 385 00:14:29,396 --> 00:14:31,153 Right? And I think we've kinda just as 386 00:14:31,153 --> 00:14:32,751 an example from this paper that we've kind 387 00:14:32,751 --> 00:14:34,290 of already talked about is 388 00:14:35,243 --> 00:14:39,399 the the number of flotation of sessions was 389 00:14:39,399 --> 00:14:42,436 associated with a positive outcome. Well, that could 390 00:14:42,436 --> 00:14:43,289 be because 391 00:14:44,129 --> 00:14:44,629 flotation 392 00:14:45,169 --> 00:14:47,509 actually improves outcomes because we're 393 00:14:47,809 --> 00:14:48,309 reducing 394 00:14:48,690 --> 00:14:51,809 the the sequel sequoia like, creating Kinase build 395 00:14:51,809 --> 00:14:54,058 and all that, or it could just be 396 00:14:54,058 --> 00:14:58,122 the animals that the clinician has a more 397 00:14:58,122 --> 00:15:01,250 positive prognosis for people are willing... The owners 398 00:15:01,250 --> 00:15:02,450 are willing to go further, 399 00:15:03,330 --> 00:15:04,690 there may be willing to invest a little 400 00:15:04,690 --> 00:15:07,089 more, and they just get more flotation sessions, 401 00:15:07,330 --> 00:15:09,737 not and not dying. And and not dying 402 00:15:09,737 --> 00:15:11,410 because. Because if you die, you're not gonna 403 00:15:11,410 --> 00:15:13,483 get many more flotation. That's true. Yeah. It's 404 00:15:13,483 --> 00:15:16,286 kinda t it. Yeah. Yes. And so So 405 00:15:16,286 --> 00:15:18,216 those 2... Like you said, we can't 406 00:15:18,589 --> 00:15:20,360 sort those things out 407 00:15:20,733 --> 00:15:21,233 retrospectively, 408 00:15:22,083 --> 00:15:23,853 but we can say that those 409 00:15:24,642 --> 00:15:27,032 1 factor and another factor are associated And 410 00:15:27,032 --> 00:15:29,604 whether they're cause and effect or they just 411 00:15:29,662 --> 00:15:31,908 moved together in the same direction But I 412 00:15:31,908 --> 00:15:33,664 think it's a good starting point to say, 413 00:15:33,903 --> 00:15:35,440 you know, and and maybe help 414 00:15:35,818 --> 00:15:38,371 drive some questions about future research we might 415 00:15:38,371 --> 00:15:39,144 do where you 416 00:15:39,584 --> 00:15:41,260 I I said, you know, we wouldn't go 417 00:15:41,260 --> 00:15:43,016 out and do an induced model, 418 00:15:43,495 --> 00:15:45,171 but it might be something where you could 419 00:15:45,171 --> 00:15:48,045 take this information and design a prospective study 420 00:15:48,045 --> 00:15:50,054 and say Okay. In the future, when we 421 00:15:50,054 --> 00:15:52,128 look at... When we have down cal cases, 422 00:15:52,766 --> 00:15:53,425 we wanna 423 00:15:53,883 --> 00:15:56,297 implement, you know, we wanna do these diagnostic 424 00:15:56,355 --> 00:15:58,052 tests, we wanna look at these pro 425 00:15:58,364 --> 00:16:00,983 factors. We wanna look at these signal issues 426 00:16:00,983 --> 00:16:03,127 and and move that forward to to drive 427 00:16:03,127 --> 00:16:05,667 a future research question. Really good point. And 428 00:16:05,667 --> 00:16:07,917 and it affects how you interpret these studies 429 00:16:07,917 --> 00:16:10,634 and you explain that really well because normally, 430 00:16:10,953 --> 00:16:12,252 I'm asking you about 431 00:16:12,951 --> 00:16:14,162 allocation to treatment groups, 432 00:16:14,878 --> 00:16:17,504 randomization or blinding, neither of which in this 433 00:16:17,504 --> 00:16:20,049 study are present nor do they matter. Right? 434 00:16:20,129 --> 00:16:21,799 Right? We don't have them. If that's not 435 00:16:21,799 --> 00:16:24,129 the type of study. However, the progression that 436 00:16:24,129 --> 00:16:26,389 you described and and I like your your 437 00:16:26,450 --> 00:16:27,910 flotation example is 438 00:16:28,370 --> 00:16:31,659 if in this study I find flotation is 439 00:16:31,659 --> 00:16:34,950 associated with a positive outcome. My next scientific 440 00:16:35,168 --> 00:16:37,640 step would be, I will do a randomized 441 00:16:37,640 --> 00:16:38,916 controlled clinical trial. 442 00:16:39,568 --> 00:16:41,475 With the same type of cases that come 443 00:16:41,475 --> 00:16:44,971 in and allocate them to either float or 444 00:16:44,971 --> 00:16:48,071 don't float and then determined did did flotation 445 00:16:48,071 --> 00:16:49,935 help. That's how we get to causality. 446 00:16:50,626 --> 00:16:52,849 Right. More so than in the retrospective study 447 00:16:52,849 --> 00:16:54,436 because you said it... Well, this could go 448 00:16:54,436 --> 00:16:56,341 either way. And I think... But 1 point, 449 00:16:56,500 --> 00:16:59,455 Brad, too, I don't want to... As a 450 00:16:59,455 --> 00:17:02,022 clinician, does that mean? I should just ignore 451 00:17:02,476 --> 00:17:04,804 this because it it lacks these 452 00:17:05,993 --> 00:17:07,992 randomization and blinding factors. And I think the 453 00:17:07,992 --> 00:17:10,069 answer is no. I I still think there's 454 00:17:10,069 --> 00:17:10,969 some important 455 00:17:11,588 --> 00:17:15,000 factors that they identified in this paper that 456 00:17:15,278 --> 00:17:17,669 moving for until we... You know, it's kinda 457 00:17:17,669 --> 00:17:19,603 1 of those things we often talk about 458 00:17:20,218 --> 00:17:22,529 being paralyzed by a lack of data. Well 459 00:17:22,529 --> 00:17:24,362 we have some data here that I think 460 00:17:24,362 --> 00:17:27,572 is the study as a retrospective study is 461 00:17:27,572 --> 00:17:30,309 still well designed. And so I wouldn't just 462 00:17:30,528 --> 00:17:33,645 ignore these because it has those limitations. I 463 00:17:33,645 --> 00:17:34,099 would just 464 00:17:34,619 --> 00:17:37,256 interpret as, okay, if I if I saw 465 00:17:37,256 --> 00:17:39,334 it downer cow beef cow tomorrow. 466 00:17:40,053 --> 00:17:41,491 There are some things in here that I 467 00:17:41,491 --> 00:17:43,065 would pay attention to. 1 is 468 00:17:43,422 --> 00:17:45,491 I already know that the prognosis is really 469 00:17:45,491 --> 00:17:47,900 bad. So I'm not gonna over pro 470 00:17:48,275 --> 00:17:50,079 a downer beef cow. But 471 00:17:50,599 --> 00:17:53,496 for example, out of this paper. If I 472 00:17:54,114 --> 00:17:56,991 did my evaluation that animal, and I am 473 00:17:56,991 --> 00:17:57,810 under the 474 00:17:58,282 --> 00:18:00,590 my initial diagnosis is calvin paralysis. 475 00:18:01,465 --> 00:18:03,136 That would change how I would manage it... 476 00:18:03,375 --> 00:18:05,307 I would probably be a little 477 00:18:05,618 --> 00:18:08,641 more optimistic about prognosis. And especially if I, 478 00:18:08,720 --> 00:18:10,709 you know, maybe if it's a younger animal 479 00:18:10,709 --> 00:18:13,512 with calvin paralysis, if a younger animal of 480 00:18:13,512 --> 00:18:15,507 calvin paralysis, and I have some an owner 481 00:18:15,507 --> 00:18:17,661 that's willing to invest in some flotation sessions. 482 00:18:17,900 --> 00:18:19,417 You know, if I start stacking some of 483 00:18:19,417 --> 00:18:20,055 these factors, 484 00:18:20,548 --> 00:18:22,458 I I might... It might change how I 485 00:18:22,458 --> 00:18:24,844 think about the case. They did a a 486 00:18:24,844 --> 00:18:27,629 great job. The way you handle these studies 487 00:18:27,629 --> 00:18:30,034 instead of blinding and randomization, we talk about... 488 00:18:30,194 --> 00:18:33,310 Do they have a clear, repeatable case definition? 489 00:18:33,789 --> 00:18:37,545 And did they handle their statistical analysis appropriately 490 00:18:37,545 --> 00:18:39,799 and they did? Both those cases. So we 491 00:18:39,799 --> 00:18:42,440 can draw some good conclusions here from a 492 00:18:42,440 --> 00:18:44,759 clinical standpoint, and and I agree with what 493 00:18:44,759 --> 00:18:45,419 you said 494 00:18:45,972 --> 00:18:48,201 a young cow with calvin paralysis is gonna 495 00:18:48,201 --> 00:18:50,668 have a different prognosis than some of the 496 00:18:50,668 --> 00:18:52,340 cows with some of the other conditions or 497 00:18:52,340 --> 00:18:54,503 an older cow, which which makes perfect sense, 498 00:18:54,662 --> 00:18:56,093 but now I can put some numbers to 499 00:18:56,093 --> 00:18:58,501 it. Yep. Yep. So as we 500 00:18:58,876 --> 00:19:01,023 come through the materials and methods here and 501 00:19:01,023 --> 00:19:03,603 and we kinda gloss over the 502 00:19:04,294 --> 00:19:06,517 experimental units because in this case, it was 503 00:19:06,517 --> 00:19:09,454 individual animal, and the data hierarchy, it was 504 00:19:09,454 --> 00:19:10,351 nested within 505 00:19:11,216 --> 00:19:12,510 each teaching hospital, 506 00:19:13,440 --> 00:19:14,949 but that's 1 of the things that, 507 00:19:15,744 --> 00:19:18,524 in the analysis, you can control for. So 508 00:19:18,524 --> 00:19:20,192 any types of hierarchy you wanna be sure 509 00:19:20,192 --> 00:19:22,274 you get in the analysis as you control 510 00:19:22,274 --> 00:19:24,421 form. And you mentioned kinda some of the 511 00:19:24,421 --> 00:19:26,670 ways that you would use this. Any other 512 00:19:26,726 --> 00:19:29,271 conclusions from this 1, Ryan? Yeah. I think 513 00:19:29,271 --> 00:19:30,089 there was 1 514 00:19:30,716 --> 00:19:32,540 and I'm just not a conclusion. This is 515 00:19:32,540 --> 00:19:34,206 specific finding out of this paper. There was 516 00:19:34,206 --> 00:19:35,872 1 thing as I'm looking back through this. 517 00:19:36,031 --> 00:19:36,927 They found was 518 00:19:37,554 --> 00:19:38,855 if if an animal 519 00:19:39,394 --> 00:19:42,194 walked out of the flotation tank after the 520 00:19:42,194 --> 00:19:44,730 first session, that was a 521 00:19:45,246 --> 00:19:47,709 very good pro agnostic indicator. And so, 522 00:19:48,662 --> 00:19:50,330 again, I'm, I think we've touched on a 523 00:19:50,330 --> 00:19:51,894 lot of the big things. You know, it's 524 00:19:52,331 --> 00:19:55,347 The the limitation is... It's a retrospective study, 525 00:19:55,982 --> 00:19:58,046 but it's a very good retrospective study, 526 00:19:58,760 --> 00:20:01,100 and they're and it gives us some new 527 00:20:01,314 --> 00:20:04,170 information about a different production class that in 528 00:20:04,170 --> 00:20:05,781 the past, we haven't had any 529 00:20:06,312 --> 00:20:08,233 information on. So, no. I think You know, 530 00:20:08,313 --> 00:20:10,066 if you if you if I were to 531 00:20:10,066 --> 00:20:11,979 somebody were to ask me to give them 532 00:20:11,979 --> 00:20:13,754 an example of a good 533 00:20:14,609 --> 00:20:15,406 retrospective study, 534 00:20:16,138 --> 00:20:17,734 I'd certainly point them to this paper. 535 00:20:18,851 --> 00:20:20,606 And then just like I said, the the 536 00:20:20,606 --> 00:20:22,600 big conclusions about beef versus dairy, 537 00:20:23,094 --> 00:20:23,594 and 538 00:20:24,213 --> 00:20:25,970 association versus c association with some of these 539 00:20:25,970 --> 00:20:26,370 factors, 540 00:20:27,328 --> 00:20:28,627 and which ones pro 541 00:20:29,086 --> 00:20:30,936 and and which ones didn't was... There were 542 00:20:30,936 --> 00:20:32,929 a few that were surprising that they weren't 543 00:20:32,929 --> 00:20:33,429 pro. 544 00:20:34,524 --> 00:20:37,155 All all great information for a clinician looking 545 00:20:37,155 --> 00:20:39,564 at at a a downer beef count. Totally 546 00:20:39,564 --> 00:20:41,721 agree. And if if you're interested, this paper 547 00:20:41,721 --> 00:20:42,461 is in 548 00:20:42,920 --> 00:20:43,420 Jv 549 00:20:43,878 --> 00:20:46,970 journal veterinary internal medicine, lead author was Perez 550 00:20:47,089 --> 00:20:50,039 El, and the title was pro indicators of 551 00:20:50,039 --> 00:20:52,591 outcome and non ambulatory beef cattle presented to 552 00:20:52,591 --> 00:20:55,882 2 referral hospitals, a retrospective study. So 553 00:20:56,275 --> 00:20:58,595 really nice job, Brian going through this. And 554 00:20:58,674 --> 00:21:00,755 I mentioned at the top, but we we... 555 00:21:00,914 --> 00:21:02,755 As we continue to do these, we do 556 00:21:02,755 --> 00:21:04,595 appreciate your feedback. And if you have a 557 00:21:04,595 --> 00:21:06,448 specific article, you'd us to take a look 558 00:21:06,448 --> 00:21:08,204 at and discuss through. I always like getting 559 00:21:08,283 --> 00:21:11,157 Brian's take on these, especially talking through some 560 00:21:11,157 --> 00:21:13,073 of the experimental design and what should be 561 00:21:13,073 --> 00:21:15,163 my take homes because it helps me sometimes 562 00:21:15,163 --> 00:21:17,875 easier than reading them fine. So I appreciate 563 00:21:17,875 --> 00:21:18,353 you doing that. 564 00:21:19,310 --> 00:21:20,906 But if you have an article you'd like 565 00:21:20,906 --> 00:21:22,660 us look at, you could certainly send us 566 00:21:22,660 --> 00:21:24,630 at bc at k u dot edu e