The accuracy of various tests for bacterial vaginosis in predicting preterm birth—a systematic review
Bibliographic record
Abstract
To determine the accuracy with which various types of tests for bacterial vaginosis (BV) predict spontaneous preterm birth in pregnant women, studies were identified, without language restrictions, through nine different databases and manual searching of bibliographies of known primary and review articles. There are four different BV testing methods: Gram-staining test using either Nugent's or Spiegel's criteria, gas liquid chromatography and clinical criteria. Two reviewers selected studies independently and extracted data on their characteristics, quality and accuracy with spontaneous preterm birth as the reference standard. Data on asymptomatic women and women with symptoms of threatened preterm labour were analysed separately. Data were pooled to produce summary estimates of likelihood ratios for positive (LR+) and negative (LR−) test results for the various types of tests. There were 18 primary articles, involving a total of 17 868 women. Meta-analysis of studies testing asymptomatic women in the second trimester showed that clinical criteria had an LR+ of 5.14 (95% confidence interval 4.44–6.15) and an LR− of 0.48 (0.42–0.55), Gram-staining (Nugent's criteria) had an LR+ of 1.64 (1.44–1.87) and an LR− of 0.88 (0.84–0.92), and Gram-staining (Spiegel's criteria) had an LR+ of 2.44 (1.36–4.98) and an LR− of 0.81 (0.64 to 1.01). Among symptomatic women, Gram-staining (Spiegel's criteria) had an LR+ of 1.29 (1.03–1.62) and an LR− of 0.85 (0.73–1.00).
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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 | 0.017 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".