Antenatal Corticosteroids for Late-Preterm Infants: A Decision-Analytic and Economic Analysis
Bibliographic record
Abstract
Objectives. Antenatal corticosteroids (ACS) are not routinely administered to patients at risk for delivery between 34 and 36 6/7 weeks. Our objective was to determine whether ACS are cost-effective for late-preterm infants at risk for imminent preterm delivery. We hypothesized that the preferred strategy <36 weeks would include ACS while the preferred strategy ≥36 weeks would not. Methods. We performed decision-analytic and cost-effectiveness analyses to determine whether ACS was cost-effective at 34, 35, and 36 weeks. We conducted a literature review to determine probability, utility, and cost estimates absent of patient-level data. Base-case cost-effectiveness analysis, univariable sensitivity analysis, and Monte Carlo simulation were performed. A threshold of $100,000/QALY was considered cost-effective. Results. The incremental cost-effectiveness ratio favored the administration of a full course of ACS at 34, 35, and 36 weeks ($62,888.25/QALY, $64,425.67/QALY, and $64,793.71/QALY, resp.). A partial course of ACS was not cost-effective. While ACS was the consistently dominant strategy for acute respiratory outcomes, all models were sensitive to changes in variables associated with chronic respiratory disease. Conclusions. Our findings suggest that the administration of ACS to patients at risk of imminent delivery 34-36 weeks could significantly reduce the cost and acute morbidity associated with late-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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".