Research in the Delivery Room: Can You Tell Me It’s Evolution?
Bibliographic record
Abstract
Many of the recommendations for newborn care in the delivery room (DR) are based on retrospective observational studies, preclinical studies of mannequins or animal models, and expert opinion. Conducting DR research is challenging. Many deliveries occur in fraught circumstances with little prior warning, making it difficult to get prospective consent from parents and buy-in from clinicians. Many DR interventions are difficult to mask for the purpose of a clinical trial and it is not easy to identify appropriate outcomes for studies that are sufficiently "short-term" that they are likely to be influenced by the intervention, yet sufficiently "long-term" to be considered clinically important. However, despite these challenges, much information has been accrued from clinical studies in recent years. In this article, we outline our experience of conducting clinical research in the DR. In our initial studies almost 20 years ago, we found wide variation in the equipment used both nationally and internationally, reflecting the paucity of evidence to support practice. This started a journey that has included many observational studies and randomized controlled trials that have attempted to refine how we care for newborn infants in the DR. Each has given further information and, inevitably, raised many more questions about the approach to caring for newborns in the DR.
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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 | 0.083 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.016 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".