Prevalence of breast muscle myopathies (spaghetti meat, woody breast, white striping) and associated risk factors in broiler chickens from Ontario Canada
Bibliographic record
Abstract
Spaghetti meat (SM), woody breast (WB), and white striping (WS) are myopathies that affect the pectoral muscle of fast-growing broiler chickens. The prevalence and possible risk factors of these myopathies have been reported in other countries, but not yet in Canada. Thus, the objective of this study was to assess the prevalence and risk factors associated with these myopathies in a representative population of Canadian broilers. From May 2019 to March 2020, 250 random breast fillets from each of 37 flocks (total, 9,250) were obtained from two processing plants and assessed for the presence and severity of myopathies. Demographic data (e.g., sex and average live weight), environmental conditions during the grow-out period (e.g., temperature), and husbandry parameters (e.g., vaccination) were collected for each flock. Associations between these factors and the myopathies were tested using logistic regression analyses. The prevalence of SM, severe WB, and mild or moderate WS was 36.3% (95% CI: 35.3-37.3), 11.8% (95% CI: 11.2-12.5), and 96.0% (95% CI: 95.6-96.4), respectively. Most (85.1%) of the fillets showed multiple myopathies. Regression analyses showed that the odds of SM increased with live weight (OR = 1.30, 95% CI 1.01-1.69) and higher environmental temperature during the grow-out period (OR = 1.75, 95% CI 1.31-2.34). The odds of WB increased with live weight (OR = 1.23, 95% CI 1.03-1.47) and when flocks were not vaccinated against coccidia (OR = 1.86, 95% CI 1.51-2.29). This study documents for the first time a high prevalence of myopathies in Ontario broilers, and suggests that these lesions may have a significant economic impact on the Canadian poultry industry. Our results indicate that environmental conditions and husbandry are associated with the development of breast myopathies, in agreement with the current literature. Future studies are needed to determine how risk factors can promote the occurrence of these conditions, in order to implement possible mitigating strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".