Bibliographic record
Abstract
We read with great interest the meta-analysis by Simcox et al. in which they concluded that prophylactic antibiotics were of no benefit in the prevention of preterm birth in high-risk women.1 In a similar meta-analysis we published this year, we reported opposite results, but our inclusion and exclusion criteria were different.2 Three criteria could explain, in part, the differences from the current meta-analysis. In our meta-analysis, randomised trials published in languages other than English were included; secondly, studies with more than 5% loss-to-follow-up were excluded as they can give a high rate of non-compliance and selection biases; and, finally, our selection was limited to investigations of patients whose treatment began during the second trimester of pregnancy. Even though inclusion and exclusion criteria can be justified for different reasons, we believe that some criteria should be considered as mandatory. For example, in one of the studies cited by Simcox et al.3 the authors stated that they included 93 patients from the same multicentre study who had already been incorporated in another previously published report.4 The latter report was also subsumed in the current meta-analysis. We believe that including the same patients twice in a meta-analysis is methodologically inappropriate. Each meta-analysis is limited by its own predetermined criteria. Based on our criteria, we found that erythromycin or clindamycin administration in the second trimester was related to a decrease in the rate of preterm birth.2 Moreover, when we looked at the individual effect of metronidazole (by excluding all studies that added erythromycin or clindamycin to metronidazole), we found that its use was associated with an increase in the rate of preterm birth. Therefore, we agree with the authors on the real possibility of harm by metronidazole to women at high risk of preterm delivery. However, we believe that there is enough evidence to suggest that clindamycin should be recommended for the treatment of bacterial vaginosis in pregnancy and has a high chance of preventing preterm delivery if given during the second trimester.5
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".