103: Neonatal Resuscitation (NR): Adherence to the Algorithm in Tertiary Care and Community Centers?
Bibliographic record
Abstract
Skilled, guideline and timeline adhered neonatal resuscitation remains the cornerstone of advanced neonatal care ensuring a favorable outcome. There is a paucity of literature on how well individual centers are able to meet the standards set out in the Neonatal Resuscitation Program (NRP). To compare the frequency of incomplete documentation and timelines for resuscitation intervention among a defined population of neonates i.e infants born at a gestational age ≥34 weeks who required positive pressure ventilation via an endotracheal tube at birth. A multicenter retrospective chart review for the duration January 2011 to December 2014 was conducted. Level II and Level III centers were compared with each other and the gold standard NRP 2011. Data on demography, timelines to resuscitation intervention, documentation and outcomes were collected and analyzed. Two-year preliminary data identified 68 cases that met the inclusion criteria. Incomplete documentation was noted across all domains with a frequency ranging between 14 to 66%. The timelines to intervention were (mean, SD) 3.9 (4.3) minutes for intubation, 4.1 (4.76) minutes for initiation of chest compressions and 47.4 (47.3) minutes for establishment of vascular access. 13 (76.4%)of the infants received chest compression prior to intubation. In comparison to previous data from real life and simulated environments on timelines for resuscitation intervention in neonates, there exists a gap between guideline adherence and clinical practice. Using this information we hope to identify areas in which centers can target intervention to improve the delivery of NRP.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".