Economic impacts of lameness in feedlot cattle1
Bibliographic record
Abstract
Abstract Lameness is an important health issue in feedlot cattle; however, there is a paucity of information regarding its economic impact. Decision tree models are excellent tools for assessing costs of disease such as the net return (net return = benefit – cost). Models were developed using expert opinion, literature and retrospective feedlot data provided by Vet-Agri Health Services (VAHS, Airdrie, Alberta, Canada) collected from 2005 to 2015 on individually treated cattle (n = 30,940) from 28 feedlots. The objective was to estimate net return of various lameness diagnoses and impacts of cattle type, season of treatment, and extreme high and low cattle prices. Cattle were diagnosed as lame according to the following categories: foot rot, foot rot in heavy cattle (BW > 363 kg at treatment), injury, lame with no visible swelling, and joint infection. Records consisted of arrival and treatment weight, cost of treatment, and cattle deaths. Records included cattle types classified as: fall calves (heifer and steer), winter calves (heifer and steer) and yearling cattle (heifer and steer). Lastly, variables ADG, days on feed (DOF), and Season (spring, summer, fall, and winter) were created. Models estimated net return using cattle slaughter prices for healthy cattle that reached a slaughter weight of 635 kg and for three possible outcomes for each diagnosis after final treatment: cattle that recovered after treatment and reached a slaughter weight of 635 kg; cattle that were removed before they reached slaughter weight; or cattle that died. Compared to undiagnosed cattle with 1.36 kg/d ADG, cattle diagnosed with foot rot and foot rot heavy cattle had the highest ADG until first treatment (1.14 and 1.57 kg/d, respectively) and differed significantly (P < 0.05) compared to cattle diagnosed with injuries (0.87 kg/d), lame with no visible swelling (0.64 kg/d), and joint infections (0.53 kg/d). Yearling steers had the most positive returns compared to all other cattle types. Cattle with lighter arrival weight had lower ADG and increased economic losses after treatment compared to heavier weighted cattle on arrival. Based on average slaughter prices over a 10-yr period for healthy cattle, return was $690. Return after final treatment for cattle with foot rot was $568, foot rot in heavy cattle was $695, and injury was $259. However, joint infections and lame with no visible swelling had negative returns of –$286 and –$701, respectively.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".