Non-antibiotic approaches at drying-off for treating and preventing intramammary infections: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Intramammary infection (IMI) treatment and prevention at drying-off is one of the leading causes for using antimicrobials on dairy farms. The objective of the current paper is to describe the protocol used for conducting a systematic review of the literature on non-antibiotic strategies that can be used on dairy cows at dry off to treat and prevent IMI. Relevant literature will be identified using a combination of database search strategies and iterative screening of references. To be included in the review, articles will have to: (1) be published after 1969; (2) be written in English, French, or Spanish; (3) use a study design such as a controlled trial, an observational study, or an experimental study conducted in vivo; (4) be conducted on commercial dairy cows; (5) investigate a non-antibiotic intervention used at dry off; and finally, (6) report on a relevant mastitis outcome. Titles and abstracts, then full articles will be reviewed for inclusion. Specific data will be extracted and risk of bias will be assessed for all included articles. The planned systematic review will be the first to colligate, in a coherent whole, studies investigating non-antibiotic strategies for treating and preventing IMI at drying-off.
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 | 0.081 | 0.105 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.024 |
| Bibliometrics | 0.013 | 0.012 |
| 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) | 0.051 | 0.008 |
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".