Antimicrobial-based dry cow therapy approaches for cure and prevention of intramammary infections: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
In dairy herds, application of antimicrobials at drying-off is a common mastitis control measure. This article describes a protocol for systematic review and meta-analysis to address three crucial points regarding antimicrobial usage at drying-off: (1) comparative efficacy of antimicrobials used for preventing new and eliminating existing intramammary infections (IMI); (2) comparison of selective and blanket dry cow therapy approaches in preventing new and eliminating existing IMI; and (3) assessment of the extra prevention against new IMI that can be gained from using antimicrobial-teat sealant combinations versus antimicrobials alone. Five PICO (Population, Intervention, Comparator, Outcome) questions were formulated to cover the three objectives of the review. Medline, CAB Abstracts, Web of Science, and conference proceedings will be searched along with iterative screening of references. Articles will be eligible if: (1) published after 1966; (2) written in English or French; and (3) reporting field clinical trials and observational studies, conducted on dairy cows at drying-off, with at least one antimicrobial-treated group and one IMI-related outcome. Authors will independently assess the relevance of titles and abstracts, extract data, and assess bias and the overall quality of evidence. Results will be synthesized and analyzed using pairwise and network meta-analysis. The proposed study will significantly update previously conducted reviews.
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 | 0.086 | 0.105 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.022 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.009 |
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".