Quality assessment of systematic reviews and meta-analyses that examine preventive antibiotic uses and management practices designed to prevent disease in livestock
Bibliographic record
Abstract
To implement effective stewardship in food animal production, it is essential that producers and veterinarians are aware of preventive interventions to reduce illness in livestock. Systematic reviews and meta-analyses (SR/MA) provide transparent, replicable, and quality-assessed overviews. At present, it is unknown how many SR/MA evaluate preventive antibiotic use or management practices aimed at reducing disease risk in animal agriculture. Further, the quality of existing reviews is unknown. Our aim was to identify reviews investigating these topics and to provide an assessment of their quality. Thirty-eight relevant reviews were identified. Quality assessment was based on the AMSTAR 2 framework for the critical appraisal of systematic reviews. The quality of most of the reviews captured was classified as critically low (84.2%, n = 32/38), and only a small percentage of the evaluated reviews did not contain critical weaknesses (7.9%, n = 3/38). Particularly, a small number of reviews reported the development of an a priori protocol (15.8%, n = 6/38), and few reviews stated that key review steps were conducted in duplicate (study selection/screening: 26.3%, n = 10/38; data extraction: 15.8%, n = 6/38). The development of high-quality reviews summarizing evidence on approaches to antibiotic reduction is essential, and thus greater adherence to quality conduct guidelines for synthesis research is crucial.
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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 | 0.027 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".