328 Assessment of chronically ill or injured feedlot cattle populations at eight United States and nine Canadian feedlots and implications for cattle welfare
Bibliographic record
Abstract
Abstract Cattle health management in the feedlot sector continues to evolve and improve cattle welfare by promoting an understanding of illness and injury to minimize their effects. Many papers have focused on treating individual ill or injured cattle in cattle feeding facilities. However, care, treatment, and outcomes for chronically ill or injured cattle (“chronic cattle”) have received little attention in the literature. The purpose of this descriptive study was to present demographics, diagnoses, and outcomes of chronic cattle in 17 feedlots in the U.S. and Canada and discuss potential risk factors and welfare implications. Individual chronic cattle information and treatment records from 17 feedlots (U.S.: 8; Canada: 9) over 7 yr (2014 – 2020) were retrospectively retrieved from a large feedlot consulting and data-management service (Feedlot Health Management Services, a division of TELUS Agriculture Solutions Inc.). Chronic cattle were defined as those that had spent time in a feedlot’s designated chronic pen. Descriptive results are presented as means ± standard deviations. U.S. feedlots averaged 31,280 ± 23,231 cattle/yr, had an average chronicity rate of 2.24 ± 2.42 % (median 1.53%), and contributed 52,709 chronic cattle to the dataset. Canadian feedlots averaged 21,913 ± 10,497 cattle/yr, had an average chronicity rate of 1.41 ± 0.82% (median 1.10%), and contributed 15,940 chronic cattle to the dataset. U.S. chronic cattle were sourced across all seasons primarily from auctions and ranches, had an average arrival weight of 163 ± 59 kg, and an average days on feed (DOF) of 362 ± 172 d. Canadian chronic cattle were primarily auction-sourced in the fall, weighed 272 ± 95 kg at arrival, and averaged 246 ± 141 DOF. Chronic cattle were treated at least once for respiratory issues (U.S.: 46.3%; Canada: 26.9%), bullers (U.S.: 34.5%; Canada: 5.8%), musculoskeletal issues (U.S.: 2.2%; Canada: 1.4%), lameness (U.S.: 1.7%; Canada: 28.1%), metabolic issues (U.S.: 0.5%; Canada: 4.5%), or other diagnoses (U.S.: 2.2%; Canada: 6.3%), with many cattle treated for multiple issues (U.S.: 12.5%; Canada: 27.0%). Outcomes for chronic cattle were shipped (U.S.: 73.1%; Canada: 52.5%), railed (U.S.: 8.1%; Canada: 20.7%), euthanized (U.S.: 0.7%; Canada: 9.9%) died unassisted (U.S.: 18.1%; Canada: 16.2%), or transferred (U.S.: < 0.1%; Canada: 0.8%). These results are some of the first to describe chronic cattle populations and present interesting trends for further analysis. For example, differences in buller and lameness rates could reflect actual differences but may be artifices of the dataset and feedlot definitions. Additionally, high marketed cattle rates (shipped and railed) indicate positive outcomes for most cattle, but variable mortality rates merit further scrutiny.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".