Equipoise with respect to wrapping premature newborns immediately after delivery
Bibliographic record
Abstract
To the Editor; We noted the recently updated neonatal resuscitation program guidelines (1) and synopsis (2) and thought it was important to clarify whether or not equipoise exists with respect to wrapping premature newborns immediately after delivery. We certainly appreciate the attention given to recent efforts to reduce hypothermia in premature newborns (3,4). As our recent systematic review illustrates (5), we are reasonably confident that wrapping reduces heat loss in this population. While there has often been a lag between evidence and practice, it seems this research has already had an impact: two recent surveys (6,7) confirm 20% to 29% of neonatal intensive care units now routinely wrap this population, albeit with great variation in how this practice is applied. We think it is important to stress that we do not yet know the long-term significance of this intervention. The Canadian Institute of Health Research and the Stollery Children's Hospital Foundation have funded a large, international, multicentre study to examine the effects on wrapping premature newborns on morbidity and mortality, which is presently underway in collaboration with the Vermont Oxford Network (8). Until the results of this trial are known, we think equipoise exists with regard to the long-term outcome of wrapping premature newborns, and we are not yet ready to recommend this practice to be included as part of the standard of care.
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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 | 0.022 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".