Bibliographic record
Abstract
Three main conditions explain preterm birth: medically indicated (iatrogenic) preterm birth (25%; 18.7–35.2%), preterm premature rupture of membranes (PPROM) (25%; 7.1–51.2%) and spontaneous (idiopathic) preterm birth (50%; 23.2–64.1%). The majority of multiple pregnancies (10% of all preterm births) are delivered preterm (50% for medical reasons). Although medical indications relate more to feto‐maternal conditions, PPROM to infections and idiopathic preterm birth to lifestyle, these risk factors are identified in any category, emphasising that preterm birth has a multifactorial origin. Still, several incidences of preterm birth are not completely explained with a plausible cause for PPROM or spontaneous preterm labour suggesting that other causes have yet to be identified. In addition, preterm birth is associated with unrecognised severe congenital anomalies. Variability within the main categories may be explained by the studied population, ethnic group, social class and preventive interventions towards reducing spontaneous preterm birth where the proportion of medically‐indicated preterm birth is increased. Despite being retrospective a classification according to gestational age at birth is important for neonatal prognosis. Preterm birth is stratified into mild preterm (32–36 weeks), very preterm (28–31 weeks) and extremely preterm (<28 weeks) with increasing neonatal mortality and morbidity. Recent studies suggested that infection was mostly responsible for extreme preterm birth, while stress and lifestyle accounted for mild preterm birth, and a mixture of both conditions contributed to very preterm birth.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".