Comparative efficacy of antimicrobials for treatment of clinical mastitis in lactating dairy cattle: a systematic review and network meta-analysis
Bibliographic record
Abstract
A systematic review and network meta-analysis were conducted to assess the relative efficacy of antimicrobial therapy for clinical mastitis in lactating dairy cattle. Controlled trials in lactating dairy cattle with natural disease exposure were eligible if they compared an antimicrobial treatment to a non-treated control, placebo, or a different antimicrobial, for the treatment of clinical mastitis, and assessed clinical or bacteriologic cure. Potential for bias was assessed using a modified Cochrane Risk of Bias 2.0 tool. From 14775 initially identified records, 54 trials were assessed as eligible. Networks were established for bacteriologic cure by bacterial species group, and clinical cure. Disparate networks among bacteriologic cures precluded meta-analysis. Network meta-analysis was conducted for trials assessing clinical cure, but lack of precision of point estimates resulted in wide credibility intervals for all treatments, with no definitive conclusions regarding relative efficacy. Consideration of network geometry can inform future research to increase the utility of current and previous work. Replication of intervention arms and consideration of connection to existing networks would improve the future ability to determine relative efficacy. Challenges in the evaluation of bias in primary research stemmed from a lack of reporting. Consideration of reporting guidelines would also improve the utility of future research.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".