Response to Antibiotic Treatment of Bacterial Vaginosis Predicts the Effectiveness of LACTIN-V (Lactobacillus crispatus CTV-05) in the Prevention of Recurrent Disease
Bibliographic record
Abstract
OBJECTIVES: Live biotherapeutic products (LBPs) containing vaginal Lactobacillus crispatus are promising adjuvant treatments to prevent recurrent bacterial vaginosis (BV) but may depend on the success of initial antibiotic treatment. METHODS: A post hoc analysis of data collected during the phase 2b LACTIN-V randomized control trial (L. crispatus CTV-05) explored the impact of clinical BV cure defined as Amsel criteria 0 of 3 (excluding pH, per 2019 Food and Drug Administration guidance) 2 days after completion of treatment with vaginal metronidazole gel on the effectiveness of an 11-week LACTIN-V dosing regimen to prevent BV recurrence by 12 and 24 weeks. RESULTS: At enrollment, 88% of participants had achieved postantibiotic clinical BV cure. The effect of LACTIN-V on BV recurrence compared with placebo differed by initial clinical BV cure status. The LACTIN-V to placebo risk ratio of BV recurrence by 12 weeks was 0.56 (95% confidence interval, 0.35-0.77) among participants with initial clinical BV cure after metronidazole treatment and 1.34 (95% confidence interval, 0.47-2.23) among participants without postantibiotic clinical BV cure. Among women receiving LACTIN-V, those who had achieved postantibiotic clinical BV cure at enrollment reached higher levels of detectable L. crispatus CTV-05 compared with women failing to achieve postantibiotic clinical BV cure. CONCLUSIONS: LACTIN-V seems to only decrease BV recurrence in women with clinical cure of BV after initial antibiotic treatment. Future trials of LBPs should consider limiting enrollment to these women.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".