Compliance with the Golden Hour bundle in deliveries attended by a specialized neonatal transport team compared with staff at non-tertiary centres
Bibliographic record
Abstract
Background: Preterm infants born at <32 weeks gestational age (GA) have increased morbidity if they are born outside tertiary centres (outborn). Stabilization and resuscitation after birth consistent with the neonatal Golden Hour practices (NGHP) are required to optimize outcomes. Objectives: To evaluate physiological outcomes of hypothermia and hypoglycaemia, and compliance with NGHP by neonatal transport team (NTT) compared with referral hospital team (RHT) during the stabilization of infants born at <32 weeks GA. Methods: A retrospective case-control study of infants born at <32 weeks GA during 2016-2019 at non-tertiary perinatal centres where the NTT attended the delivery (cases) were matched to infants where the RHT team attended the delivery (controls). Results: During the 4-year period, NTT team received 437 requests to attend deliveries at <32 weeks GA and attended 76 (17%) prior to delivery. These cases were matched 1:1 with controls composed of deliveries attended by the RHT. The rate of hypothermia was 15% versus 29% in the NTT and RHT groups, respectively (P = 0.01). The rate of hypoglycaemia (<2.2 mmol/L) was 5% versus 12% in the NTT and RHT groups, respectively (P = 0.64). For compliance with the NGHP, use of fluid boluses was 8% versus 33%, use of thermoregulation practices, that is, plastic bag, was 76% versus 21%, and establishment of intravenous access was 20 min versus 47 min, in the NTT and RHT groups, respectively. Conclusions: High-risk preterm deliveries attended by the NTT compared with the RHT had increased compliance and earlier implementation of the NGHP elements, associated with improved physiological stability and lower hypothermia rates. Outreach education for RHT should ensure that these key elements are included during the training in the stabilization of high-risk preterm deliveries.
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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 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".