PSVIII-1 Identifying factors associated with the incidence of tibial dyschondroplasia in poultry
Bibliographic record
Abstract
Abstract Selecting on increased breast meat yield and body weight (BW) is thought to be associated with the increasing prevalence of leg disorders. A disorder of particular importance is tibial dyschondroplasia (TD), which is characterized as uncalcified cartilage in the proximal area of the tibiotarsal bone, leading to increased lameness, reduced feed and water intake, and subsequently poorer production efficiency and increased pre-slaughter mortality. Although it is hypothesized that TD is more prevalent in fast-growing poultry, this relationship can vary across studies making it challenging to distill the relationship between BW and TD, especially as studies of TD span several decades. Therefore, a systematic literature search and meta-analysis was conducted to identify factors influencing TD incidence. To date, the systematic literature search has resulted in 21 eligible papers, although data are still being extracted from the literature. Data extracted from each study included TD incidence (%), BW (kg), species (turkeys vs. broilers), sex (male vs. female), and age (weeks) of the animals. Preliminary univariable models have been developed based on the current database. Univariable generalized linear mixed models were developed in SAS (PROC GLIMMIX) including study as a random effect to evaluate the relationship between TD incidence and BW, species, sex, and age. Turkeys (27.3 ± 0.08%) had a greater TD incidence than broilers (19.3 ± 0.03%); however, this difference was not significant (P = 0.3481). There was a tendency for age to influence TD incidence (P = 0.0553) and a significant effect of BW (P < 0.0001). TD incidence also tended to differ between the sexes, with greater incidence in males (22.9 ± 0.07%) than females (11.3 ± 0.05%; P = 0.0657). After extracting all available data from the literature, further analysis will be conducted to consider multivariable models to predict TD. As such, caution should be used when generalizing these results due to the preliminary nature of the database. With the currently available data, the results of this study support the relationship between BW/size and TD. Furthermore, this study illustrates that TD is a prevalent challenge in both major poultry species. Reducing TD incidence could significantly improve animal welfare and mitigate economic losses associated with pre-slaughter mortality.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".