Antenatal corticosteroid prophylaxis at late preterm gestation: Clinical guidelines vs clinical practice
Bibliographic record
Abstract
Objective: To investigate whether the Antenatal Late Preterm Steroids (ALPS) trial, has been translated into clinical practice in Canada and the United States. Temporal trends in optimal and suboptimal antenatal corticosteroid (ACS) use among late preterm deliveries were also assessed. Design: A retrospective cohort study. Setting: USA and Canada, 2007 to 2020. Population: All live births in the US (n= 32,476,039) and Nova Scotia, Canada (n= 116,575). Methods and Main outcome measured: Using data from the Natality database and the Nova Scotia Atlee Perinatal Database, ACS administration within specific categories of gestational age was assessed by calculating rates per 100 live births. Temporal trends in optimal, and suboptimal ACS use were also assessed. Results: In Nova Scotia, the rate of any ACS administration increased significantly among women delivering at 35-36 weeks, from 15.2% in 2007-2016 to 19.6% in 2017-2020 (OR 1.36, 95%CI 1.14, 1.62). In the U.S., among live births at 35-36 weeks’ gestation, any ACS use increased from 4.1% in 2007–2016 to 18.5% in 2017–2020 (OR 5.33, 95% CI 5.28–5.38). Among infants between 24 and 34 weeks’ gestation in Nova Scotia, 32% received optimally timed ACS, while 47% received ACS with suboptimal timing. Of the women who received ACS in 2020, 34% in Canada and 20% in the United States delivered at ≥37 weeks. Conclusion: Publication of the ALPS trial resulted in increased ACS administration at late preterm gestation in Nova Scotia, Canada and the U.S.. However, a significant fraction of women receiving ACS prophylaxis delivered at term gestation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".