Phenotypic Classification of preterm Birth Among Multiparous Women: A Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: The Global Alliance to Prevent Prematurity and Stillbirth developed a phenotypic classification for preterm birth using clinical presentation (rather than risk factors) to improve surveillance. The objective of this study was to determine distributions of preterm birth phenotypes and associations with Caesarean section, low Apgar score, and neonatal death in multiparous women, stratifying by first versus recurrent preterm births. METHODS: This population-based cohort study used the Better Outcomes Registry and Network (BORN) of multiparous women giving birth in hospital with a singleton after 20 weeks in Ontario from 2012 to 2014 (Canadian Task Force Classification II-2). RESULTS: In multiparous women with preterm birth, 29.6% had a history of recurrence, of whom 66.2% had at least one clinical condition associated with the phenotypic model, compared with 63.5% of first preterm births. In recurrent preterm births, criteria for maternal, fetal, and placental conditions were met in 44.5%, 37.9%, and 8.2%, respectively, compared with 36.8%, 39.0%, and 10.4%, respectively, of first preterm births. Associations of preterm birth with Caesarean section, low Apgar score, and neonatal death varied across clinical conditions but were similar between first and recurrent preterm births; for example, for recurrent preterm birth, Caesarean section for maternal, fetal, and placental conditions had odds ratios of 1.66 (95% confidence interval [CI] 1.32-2.07), 1.09 (95% CI 0.80-1.49), and 3.92 (95% CI 1.98-7.78), compared with first preterm birth odds ratios of 1.21 (95% CI 1.03-1.41), 0.92 (95% CI 0.77-1.10), and 6.24 (95% CI 4.07-9.56). CONCLUSION: This study provides novel evidence of the utility of the preterm birth phenotypic classification model by using stratification for previous preterm birth, a robust predictor-with variation in phenotypes in initial and recurrent preterm births.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".