Thermoregulation and golden hour practices in extremely preterm infants: an international survey
Bibliographic record
Abstract
BACKGROUND: Are thermoregulation and golden hour practices in extremely preterm (EP) infants comparable across the world? This study aims to describe these practices for EP infants based on the neonatal intensive care unit's (NICUs) geographic region, country's income status and the lowest gestational age (GA) of infants resuscitated. METHODS: The Director of each NICU was requested to complete the e-questionnaire between February 2019 and August 2021. RESULTS: We received 848 responses, from all geographic regions and resource settings. Variations in most thermoregulation and golden hour practices were observed. Using a polyethylene plastic wrap, commencing humidity within 60 min of admission, and having local protocols were the most consistent practices (>75%). The odds for the following practices differed in NICUs resuscitating infants from 22 to 23 weeks GA compared to those resuscitating from 24 to 25 weeks: respiratory support during resuscitation and transport, use of polyethylene plastic wrap and servo-control mode, commencing ambient humidity >80% and presence of local protocols. CONCLUSION: Evidence-based practices on thermoregulation and golden hour stabilisation differed based on the unit's region, country's income status and the lowest GA of infants resuscitated. Future efforts should address reducing variation in practice and aligning practices with international guidelines. IMPACT: A wide variation in thermoregulation and golden hour practices exists depending on the income status, geographic region and lowest gestation age of infants resuscitated. Using a polyethylene plastic wrap, commencing humidity within 60 min of admission and having local protocols were the most consistent practices. This study provides a comprehensive description of thermoregulation and golden hour practices to allow a global comparison in the delivery of best evidence-based practice. The findings of this survey highlight a need for reducing variation in practice and aligning practices with international guidelines for a comparable health care delivery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".