Table_2_Relative Efficacy of Dry-Off Antimicrobial Treatments in Dairy Cattle to Cure Existing Intramammary Infections: A Systematic Review and Network Meta-Analysis.DOCX
Bibliographic record
Abstract
<p>This systematic review and network meta-analysis aimed to estimate the relative efficacy of dry cow antimicrobial therapies, registered in Canada and/or the United States, to cure existing intramammary infections (IMI) in dairy cattle. The controlled trials examining all-cause cures of existing IMI present at dry-off were eligible. Five databases and four conference proceeding platforms were searched. The risk of bias at the level of the outcome was assessed using the Cochrane 2.0 risk of bias instrument (Cochrane, Denmark), and the overall confidence in the findings from the network meta-analysis was assessed using the Confidence in Network Meta-Analysis (CINeMA) platform. Of 3,743 articles screened for eligibility by the two independent reviewers, 58 trials were included in the Bayesian network meta-analysis for the all-cause cure of existing IMI from dry-off to calving. No antimicrobial treatment (non-active control) was associated with a decreased risk of a cure compared with all other currently labeled antimicrobials in Canada and the United States; however, lack of replication trials for some antimicrobial products created large credibility intervals and, therefore, we were unable to identify meaningful comparisons between the products. Poor reporting of trial features, heterogeneity in outcome measurements, and high risk of bias in some domains further contributed to this inability to compare antimicrobials. Continued improvement in the reporting of animal trials is required to make recommendations for antimicrobial products on the basis of efficacy.</p><p>Systematic Review Registration:https://atrium.lib.uoguelph.ca/xmlui/bitstream/handle/10214/16236/Protocol_NMA_efficacy_dryoff_antibiotics_cure_IMI.pdf?sequence=3&isAllowed=y.</p>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".