The changing epidemiology of preterm labour and delivery: A systematic literature review
Bibliographic record
Abstract
Abstract Objective To identify and synthesize epidemiologic data for preterm labour (PTL) and preterm birth (PTB). Methods A systematic search, with protocol registered in PROSPERO, was implemented in MEDLINE and EMBASE and supplemented by web‐based and grey literature searches. Observational, population‐based studies in the United States, Canada, United Kingdom, France, Germany, Spain, and Italy, published in English between 2012 and 2022 were considered for inclusion. Estimates by country were reported and stratified by gestational age, birth plurality, and race/ethnicity, data permitting. Results Ten publications and nine grey literature reports were included. Epidemiologic estimates of PTL were reported for the United Kingdom and France: PTL was diagnosed in 2.2% of pregnancies and preceded 50% of PTBs. PTB rates were reported for the United States, Canada, United Kingdom, France, and Spain. Among live births in these countries, annual PTB incidence ranged from 5.9% (Spain, 2020) to 10.2% (United States, 2019). Most countries reported the PTB incidence by gestational age; reports by birth plurality or race/ethnicity were scarce. PTB rates for Germany or Italy were not identified. Conclusions While PTBs were well‐reported overall and by gestational age, how rates varied by plurality, race/ethnicity and etiology is unclear. Epidemiologic estimates for PTL, a leading cause of PTB, were rarely reported in the literature. Population‐based research is needed to understand the burden of PTL and for decision making regarding the management of this condition.
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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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".