Classification and heterogeneity of preterm birth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it