Association between increased antenatal vaginal pH and preterm birth rate: a systematic review
Bibliographic record
Abstract
Background Worldwide, 14.9 million infants (11%) are born preterm each year. Up to 40% of preterm births (PTBs) are associated with genital tract infections. The vaginal pH can reflect changes in the vaginal milieu and, if elevated, indicates an abnormal flora or infection. Objective The aim of the study was to investigate whether an increased antenatal vaginal pH >4.5 in pre-labour pregnant women is associated with an increased PTB rate <37 completed weeks gestation. Search strategy Key databases included SCOPUS, EMBASE, MEDLINE, PsycInfo and the Cochrane Central Register of Controlled Trials, complemented by hand search, up to January 2017. Selection criteria Primary research reporting vaginal pH assessment in pre-labour pregnant women and PTB rate. Data collection and analysis Data extraction and appraisal were carried out in a pre-defined standardised manner, applying the Newcastle-Ottawa scale (NOS) and Cochrane risk of bias tool. Analysis included calculation of risk difference (RD) and narrative synthesis. It was decided to abstain from pooling of the studies due to missing information in important moderators. Main results Of 986 identified records, 30 were included in the systematic review. The risk of bias was considered mostly high (40%) or moderate (37%). Fifteen studies permitted a calculation of RD. Of these, 14 (93%) indicated a positive association between increased antenatal vaginal pH and PTB (RD range: 0.02-0.75). Conclusion An increased antenatal vaginal pH >4.5 may be associated with a higher risk for PTB. It is recommended to conduct a randomised controlled trial (RCT) to investigate the effectiveness of antenatal pH screening to prevent PTB. Tweetable abstract Pregnant women with an increased vaginal pH >4.5 may be at higher risk to experience preterm birth.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| 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 | 0.000 | 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".