114 The Development and Audit of a Multidisciplinary Neonatal Resuscitation Team at a Tertiary Perinatal Centre
Bibliographic record
Abstract
This study describes the performance and risk management of a multidisciplinary NRT at a tertiary perinatal centre. The interdisciplinary nature of the Neonatal Resuscitation Program has stimulated development of neonatal resuscitation teams (NRTs). In May 2001, the training and preceptorship of an NRT was instituted at the newly-built regional perinatal centre in St. John's, Newfoundland and Labrador. In February 2002 the role of neonatal resuscitation was transferred from attending pediatricians/neonatologists to a NRT which consisted of a neonatal nurse, a respiratory therapist, and, when available, a resident or nurse practitioner. Criteria for levels of risk were established, mandating NRT attendance at moderate- and high-risk deliveries (in the latter case, accompanied by a neonatologist). Low-risk deliveries remained the responsibility of caseroom staff, assisted by the NRT when concerns arose. Liberal thresholds for risk were required to satisfy obstetrical concerns. NRT activity was prospectively audited for 18 months. During this time the NRT attended 2370 (64.3%) out of 3684 deliveries. 98% of attendances were in advance of delivery. Table 1 demonstrates the demographics of this population, with resuscitation interventions and outcomes. There were no neonatal deaths or morbidities related to resuscitation in the low- or moderate-risk groups. Neonatologists were not notified for 82 (29%) high-risk deliveries. In 72 cases the NRT felt designation of high-risk was inappropriate. The remaining 10 cases resulted in a redefining of high-risk criteria and reinforcement of communication strategies. A NRT can perform effectively in a tertiary centre with support from experienced pediatric staff at high-risk deliveries only. Categorization of deliveries into level of risk provides a safe and efficient means of delivering neonatal resuscitation services.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".