The Co-OPT international birth cohort to study the effects of antenatal corticosteroids
Bibliographic record
Abstract
ABSTRACT Introduction: Antenatal corticosteroids (ACS) are widely prescribed to improve outcomes from preterm birth. Significant knowledge gaps exist surrounding safety, optimal dosage and long-term effects of ACS, and potential unnecessary treatment with ACS is a growing concern. The Consortium for the Study of Pregnancy Treatments (Co-OPT) aims to answer research questions on the safety of medications in pregnancy; its initial focus is ACS. Objectives: 1. Generate an international birth cohort using population-level data on ACS use and pregnancy, neonatal and childhood outcomes. 2. Evaluate use of ACS and trends over time. Methods: The Co-OPT ACS cohort contains 2.3 million births between 1990 and 2019, harmonising data from four national birth registers (Finland, Iceland, Nova Scotia and Scotland) and one hospital database (Israel), with data linkage to death and medical registers. Data are stored in the NHS Scotland Safe Haven. Results: Of all babies, 72,491 (3.6%) received ACS, with similar exposure across countries, and a gradual increase over the time studied, from 1.3 to 5.6% of all births. Of all babies born before 34 weeks, 27,184 (69.8%) received ACS. Of all ACS-exposed babies, 19,411 (26.8%) were born beyond 37 weeks. Conclusion: This is the largest international birth cohort comprising data on ACS exposure with longitudinal follow-up data for 1.64 million livebirths. Most preterm babies received ACS, but over a quarter of all babies exposed to ACS were term-born, and likely received unnecessary treatment. The Co-OPT ACS cohort provides a robust platform to understand how to target and optimise ACS therapy.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".