The efficacy of litter management strategies to prevent morbidity and mortality in broiler chickens: a systematic review and network meta-analysis
Bibliographic record
Abstract
A systematic review and network meta-analysis (NMA) were conducted to address the question, 'What is the efficacy of litter management strategies to reduce morbidity, mortality, condemnation at slaughter, or total antibiotic use in broilers?' Eligible studies were clinical trials published in English evaluating the efficacy of litter management in broilers on morbidity, condemnations at slaughter, mortality, or total antibiotic use. Multiple databases and two conference proceedings were searched for relevant literature. After relevance screening and data extraction, there were 50 trials evaluating litter type, 22 trials evaluating litter additives, 10 trials comparing fresh to re-used litter, and six trials evaluating floor type. NMAs were conducted for mortality (61 trials) and for the presence or absence of footpad lesions (15 trials). There were no differences in mortality among the litter types, floor types, or additives. For footpad lesions, peat moss appeared beneficial compared to straw, based on a small number of comparisons. In a pairwise meta-analysis, there was no association between fresh versus used litter on the risk of mortality, although there was considerable heterogeneity among studies (I2 = 66%). There was poor reporting of key design features in many studies, and analyses rarely accounted for non-independence of observations within flocks.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".