144 The Epidemiology of Hoof-related Lameness in Western Canadian Feedlot Cattle
Bibliographic record
Abstract
Abstract The epidemiology of hoof-related lameness (HRL) in western Canadian feedlots, with a focus on digital dermatitis (DD), was described and analyzed to help inform recommendations on lameness control and prevention in western Canadian feedlot cattle. The retrospective data in this study were accessed from 28 western Canadian feedlots that placed cattle in 2014–2018, inclusive. The total population for this study was 1,796,176 cattle, with an annual placement average of 12,830 cattle per feedlot. These data were accessed through iFHMS Consolidated Database, provided by Feedlot Health Management Services by TELUS Agriculture, and manipulated using Microsoft® Office Access 365 ProPlus and Microsoft® Office Excel 365 ProPlus. Epidemiological analyses determined that lameness accounts for 25.7% of all treatments in western Canadian feedlots. Of those treatments, 71.7% are localized to the hoof, corresponding to 18.6% of all treatments. The most common HRL diseases are infectious bovine pododermatitis [foot rot (FR)]; digital dermatitis (DD), also known as hairy-heel wart or strawberry foot rot; and toe-tip necrosis syndrome (TTNS). These diseases account for 89.6%, 7.9% and 2.4% of HRL, respectively. Between 2014 and 2018, HRL prevalence ranged between 1.93% and 3.09% of the population, with FR consistently having the highest prevalence and TTNS the lowest. HRL and DD were tested for their associations with several animal-level risk factors using © Ausvet 2021 Epitools software. The resultant crude, univariate odds ratio values, evaluated at 95% confidence, are summarized in Table 1. Based on this analysis, acquisition source has the largest influence on the odds of developing HRL and DD, followed by population size, and placement quarter. Using SAS® (Version 9.4, SAS Institute Inc, Cary, North Carolina) statistical software, these preliminary findings will be subjected to a multivariate statistical model, which will provide adjusted OR values and statistical significance for the data in this study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".