The Risk of Preterm Birth in Women with Three Consecutive Deliveries—The Effect of Number and Type of Prior Preterm Births
Bibliographic record
Abstract
Background: We aimed to explore the association of the number, order, gestational age and type of prior PTB and the risk of preterm birth (PTB) in the third delivery in women who had three consecutive singleton deliveries. Methods: A retrospective cohort study of all women who had three consecutive singleton births at a single medical center over a 20-year period (1994–2013). The primary outcome was PTB (<37 weeks) in the third delivery. Results: 4472 women met inclusion criteria. The rate of PTB in the third delivery was 4.9%. In the adjusted analysis, the risk of PTB was 3.5% in women with no prior PTBs; 10.9% in women with prior one PTB only in the first pregnancy; 16.2% in women with prior one PTB only in the second pregnancy; and 56.5% in women with prior two PTBs. A similar trend was observed when the outcome of interest was spontaneous PTB and when the exposure was limited to prior spontaneous or indicated PTB. Conclusions: In women with a history of PTB, the risk of recurrent PTB in subsequent pregnancies is related to the number and order of prior PTBs. These factors should be taken into account when stratifying the risk of PTB.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".