Critically important antimicrobials are generally not needed to treat nonsevere clinical mastitis in lactating dairy cows: Results from a network meta-analysis
Bibliographic record
Abstract
There is ongoing debate regarding whether critically important antimicrobials (CIA) should be used to treat infections in food-producing animals. In this systematic review, we determined whether CIA and non-CIA have comparable efficacy to treat nonsevere bovine clinical mastitis caused by the most commonly reported bacteria that cause mastitis worldwide. We screened CAB Abstracts, Web of Science, MEDLINE, Scopus, and PubMed for original epidemiological studies that assessed pathogen-specific bacteriological cure rates of antimicrobials used to treat nonsevere clinical mastitis in lactating dairy cows. Network models were fit using risk ratios of bacteriological cure as outcome. A total of 30 studies met inclusion criteria. Comparisons of cure rates demonstrated that CIA and non-CIA had comparable efficacy for treatment of nonsevere clinical mastitis in dairy cattle. Additionally, for cows with nonsevere clinical mastitis caused by Escherichia coli and Klebsiella spp., bacteriological cure rates were comparable for treated versus untreated cows; therefore, there was no evidence to justify treatment of these cases with CIA. Our findings supported that CIA in general are not necessary for treating nonsevere clinical mastitis in dairy cattle, the disease that accounts for the majority of antimicrobial usage in dairy herds worldwide. Furthermore, our findings support initiatives to reduce or eliminate use of CIA in dairy herds.
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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 | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.046 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| 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".