Evaluating the efficacy of internal teat sealants at dry-off for the prevention of new intra-mammary infections during the dry-period or clinical mastitis during early lactation in dairy cows: A protocol for a systematic review and cumulative meta-analysis
Bibliographic record
Abstract
With growing scrutiny of antibiotic usage broadly among health and agricultural sectors, the dairy industry experiences increasing pressure to implement novel, evidence-based antibiotic use practices as they develop. Antibiotic administration for the prevention or treatment of intramammary infections (IMI) and mastitisis a large component of antibiotic use in the dairy industry (Lam et al., 2012). Adoption of antibiotic alternatives can contribute to reduced dairy industry antibiotic use and more effective antimicrobial stewardship.Identifying the efficacy of teat sealants as a non-antibiotic treatment alternative for the prevention of IMI and mastitis was the rationale for the original protocol (Sargeant et al., 2018) for the systematic review and network meta-analysis conducted by Winder et al. (2019). The current review protocol describes an update to the search strategy to identify relevant articles published after the original search, modified eligibility, and adds a temporal component to the existing knowledge via cumulative meta-analysis.
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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 | 0.037 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".