Antenatal Corticosteroid Administration and Childhood Respiratory Morbidity: A Regression Discontinuity Study
Bibliographic record
Abstract
OBJECTIVE: To determine if routine administration of antenatal corticosteroids affects the risk of infant lower respiratory tract infection and/or childhood asthma. DESIGN: Linked population-based cohort analysed using a regression discontinuity design, which better controls for confounding than standard observational studies. SETTING: British Columbia, Canada. POPULATION: Singleton pregnancies with a maternal admission for delivery between 31 + 0 and 36 + 6 weeks' gestation from 2000 to 2016, with follow-up to 2020. METHODS: We estimated if risks of childhood respiratory outcomes differed between pregnancies admitted just before the Canadian recommended clinical cut-off for antenatal corticosteroid administration of 34 + 0 weeks gestation (i.e., with higher probability of exposure to antenatal corticosteroids; 'exposed') than those admitted just after this cut-off (i.e., with lower probability of exposure; 'unexposed') using log binomial regression (infant lower respiratory infection hospitalisation) and pooled log binomial regression (asthma). MAIN OUTCOME MEASURES: Infant lower respiratory tract infection hospitalisation, inpatient or outpatient asthma diagnosis at 1-18 years. RESULTS: In our cohort of 21 965 children, 412 (1.9%) infants were hospitalised with a lower respiratory tract infection and 2287 (10.4%) were diagnosed with asthma. Routine administration of antenatal corticosteroids was not associated with infant lower respiratory tract infection (risk ratio = 0.95 [95% CI: 0.61, 1.37], risk difference = -0.15 excess cases per 100 [95% CI: -1.30, 0.99]) or childhood asthma (rate ratio = 1.08 [95% CI: 0.88, 1.24] 5.49 excess cases per 100 by age 13 years [95% CI: -1.78, 14.39]). CONCLUSIONS: We found no evidence that routine administration of antenatal corticosteroids affects the risk of later childhood respiratory illnesses.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".