Antenatal Corticosteroids for Late Preterm Labor
Bibliographic record
Abstract
in the initial set of studies.For example, the outcomes of women with preeclampsia/hypertension were worse as the perinatal mortality rate was 31.6 vs 15.4%.It was found later that this was a result of trying not to deliver these patients despite worsening clinical status and not because of the corticosteroids per se.Another area was the twins' pregnancy.There was a trend for improvement but until recently it History a n d BackgroundThe story of corticosteroids for the management of prematurity is quite interesting and educational.Sir Liggins was working on the induction of labor when he started his experiments with corticosteroids. 1 He found that corticosteroids had no tocolytic effect.He also noticed that the drug had a pronounced effect on the respiratory systems of the kids and improved survival.Once he confirmed his observations in the sheep model, he performed a randomized controlled study (RCT) on humans.He found, as he expected by now, that the therapy improved neonatal outcomes, especially respiratory status, significantly. 2 His work was replicated with several other RCTs and the results were confirmed again and again.The results of these RCTs have been seen by most of us repeatedly as they are now the famous logo of the Cochrane database.Interestingly, there were few researchers who changed his dose and schema of therapy so we know a lot about this specific dose and very little about lower doses that may have the same effect.It is likely that lower doses may have clinical effects as it has been shown that a partial course with only one dose still has therapeutic effects.There were other puzzling results 1,
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".