Clinical and cervical cytokine response to treatment with oral or vaginal metronidazole for bacterial vaginosis during pregnancy: a randomized trial
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of oral versus vaginal metronidazole treatment in pregnant women with bacterial vaginosis, and to compare cytokine profiles (interleukin-1beta, -6, and -8) in the cervical secretions of these women before and after treatment. METHODS: Pregnant women with bacterial vaginosis diagnosed both by Gram stain and clinical criteria were randomized to receive oral (n=52) or vaginal (n=50) metronidazole therapy. Cervical specimens for cytokine analysis and vaginal fluid for evaluation of bacterial vaginosis were obtained at baseline and 4 weeks after treatment. RESULTS: There was no significant difference in therapeutic cure rates (defined as a Gram stain score of 0-3 and the absence of all four clinical signs of bacterial vaginosis) between the two groups (71% and 70% for the oral and vaginal groups, respectively, P=1.0). Cervical levels of interleukin-1beta, -6, and -8 were significantly lower after treatment among the 72 women cured of bacterial vaginosis (P<.001, P=.001, and P=.02, respectively) but not among women who failed to respond to therapy. For interleukin-1beta and -6, a significant decrease in cytokine level was observed in both the oral and vaginal treatment groups. CONCLUSION: One week of oral metronidazole and 5 days of intravaginal metronidazole are equally efficacious for treatment of bacterial vaginosis during pregnancy. The decrease in cervical interleukin-1beta, -6, and -8 levels among women who established a normal flora after treatment but not among those with persistent bacterial vaginosis suggests a direct linkage between vaginal flora abnormalities and elevated cervical levels of interleukin-1beta, -6, and -8.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".