Non-antimicrobial approaches at drying-off for treating and preventing intramammary infections in dairy cows. Part 1. Meta-analyses of efficacy of using an internal teat sealant without a concomitant antimicrobial treatment
Bibliographic record
Abstract
Use of antimicrobial approaches at drying-off for preventing new intramammary infections (IMI) during the dry period in dairy cows could be replaced by non-antimicrobial approaches. Such approaches would be of interest not only for organic but also for conventional dairy producers. The objective of the current review was to quantify the effect of non-antimicrobial internal teat sealant (ITS)-based approaches at drying-off for treating and preventing IMI, when compared with no treatment or with an antimicrobial-based approach. The protocol for this review was published before initiating the review. A total of 18 trials from 16 articles could be used to investigate the effect of an ITS-based approach. With the available results, we conclude with a high level of confidence that non-antimicrobial ITS-based dry-off approaches are efficient for preventing new IMI during the dry period when compared with no treatment, and would reduce risk of new IMI by 52%. Moreover, we are relatively confident that a bismuth subnitrate-based ITS performed better than an antimicrobial for preventing new IMI during the dry period (a risk reduction of 23%). Similarly, we are relatively confident that an ITS-based approach would only slightly or not at all reduce the prevalence of IMI at calving compared with untreated quarters.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.033 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".