Salivary progesterone as a biomarker for predicting preterm birth: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Problem Several biomarkers have been studied to predict spontaneous preterm birth, including salivary progesterone. However, due to limited studies, the utility of this biomarker remains controversial. This study synthesized the available literature and determined the role of salivary progesterone as a potential biomarker for preterm birth. Method of Study All studies reporting the levels of salivary progesterone among pregnant women with reported birth outcomes were obtained from Ovid Medline, Scopus, CINAHL, and Cochrane Central Register of Controlled Trials from inception to January 10, 2022. A review of titles and abstracts was done independently by three reviewers. The quality of the studies was assessed using the Newcastle‐Ottawa scale. Meta‐analysis was performed in R v.4.1.3 using the “meta” package. Results Five studies involving 861 pregnant patients from Egypt, India, Iraq, and the US were included in this meta‐analysis. The random‐effects model showed that salivary progesterone level after the 28th week of gestation was significantly different between patients with term and preterm birth (standardized mean difference [SMD]: 1.99; 95% confidence interval [CI]: .44–3.54) with a high degree of heterogeneity ( I 2 = 96%, P < .001). Based on the results of this meta‐analysis, women with term birth had higher level of salivary progesterone after the 28th week of gestation than those with preterm birth. Conclusion In conclusion, the study results suggest that low salivary progesterone level after the 28th week of gestation is significantly associated with preterm birth. This study also highlights the use of saliva samples for monitoring sex steroid hormones throughout pregnancy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".