Network meta-analysis based ranking of dry off interventions to cure or prevent intramammary infections in dairy cows
Bibliographic record
Abstract
This study aimed to rank dry off interventions for the prevention of new intramammary infections (IMI) and the cure of existing IMI in quarters of dry cows using two network meta-analyses. Randomized controlled trials reported in 137 papers were assessed for inclusion eligibility. Network meta-analyses were performed separately for the incidence risk of IMI and cure risk of IMI. For cure of IMI, 29 trials with 10 dry off interventions were included. Both selective and blanket dry cow therapy, either in combination with an internal teat sealant or as a singular intervention, resulted in a better cure risk compared with the non-antimicrobial interventions. No differences were observed between the antimicrobial based interventions. For the incidence risk of IMI, 54 trials were included, representing 18 dry off interventions. The incidence risk of IMI was similar for the various selective dry cow treatments when antimicrobials were administered together with an internal teat sealant, either at quarter or cow level. Also, they did not differ from the evaluated blanket dry cow treatment interventions or when an internal teat sealant was applied alone. Selective dry cow therapy with internal teat sealant is therefore likely a suitable intervention option to simultaneously maintain a low incidence risk of IMI and a high cure risk of IMI, all the while lowering the antimicrobial use in dairy herds. Circumstances in the herd, including the distribution and prevalence of mastitis pathogens, should be evaluated before results are utilized in dairy practice given the heterogeneity of included studies.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.036 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".