Editorial: Systematic reviews reveal a need for more, better data to inform antimicrobial stewardship practices in animal agriculture
Bibliographic record
Abstract
This editorial summarizes the key observations from a special issue of Animal Health Research Reviews comprising 14 articles related to the efficacy of antimicrobial and non-antimicrobial approaches to reduce disease in beef, dairy cattle, swine, and broiler chickens. The articles used evidence-based methods, including scoping reviews, systematic reviews, meta-analyses, and network meta-analyses. Despite finding evidence of efficacy for some of the interventions examined, across the body of research, there was a lack of replication and inconsistency in outcomes among the included trials, and concerns related to completeness of reporting and trial design and execution. There is an urgent need for more and better data to inform antimicrobial stewardship practices in animal agriculture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".