Vaginal bleeding in pregnancy and adverse clinical outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Vaginal bleeding during pregnancy has been recognised as a significant risk factor for adverse pregnancy outcomes.This study aimed to investigate the association between vaginal bleeding during the first trimester of pregnancy and clinical adverse effects using a systematic review and meta-analysis.Methods: Databases of Scopus, Web of Science, PubMed (including Medline), Cochrane Library and Science Direct were searched until June of 2023.Data analysis using statistical test fixed-and randomeffects models in the meta-analysis, Cochran and meta-regression.The quality of the eligible studies was assessed by using the Newcastle-Ottawa Scale checklist (NOS).Results: A total of 46 relevant studies, with a sample size of 1,554,141 were entered into the meta-analysis.Vaginal bleeding during the first trimester of pregnancy increases the risk of preterm birth (OR: 1.8, CI 95%: 1.6-2.0),low birth weight (LBW; OR: 2.0, CI 95%: 1.5-2.6),premature rupture of membranes (PROMs; OR: 2.3, CI 95%: 1.8-3.0),abortion (OR: 4.3, CI 95%: 2.0-9.0),stillbirth (OR: 2.5, CI 95%: 1.2-5.0),placental abruption (OR: 2.2, CI 95%: 1.4-3.3)and placenta previa (OR: 1.9, CI 95%: 1.5-2.4).Conclusions: Vaginal bleeding in the first trimester of pregnancy is associated with preterm birth, LBW, PROMs, miscarriage, stillbirth, placental abruption and placenta previa.Therefore, physicians or midwives need to be aware of the possibility of these consequences and manage them when they occur.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".