After nectarine: how should we provide anesthesia for neonates?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Neonates have a high risk of perioperative morbidity and mortality. The NEonate and Children audiT of Anaesthesia pRactice IN Europe (NECTARINE) investigated the anesthesia practice, complications and perioperative morbidity and mortality in neonates and infants <60 weeks post menstrual age requiring anesthesia across 165 European hospitals. The goal of this review is to highlight recent publications in the context of the NECTARINE findings and subsequent changes in clinical practice. RECENT FINDINGS: A perioperative triad of hypoxia, anemia, and hypotension is associated with an increased overall mortality at 30 days. Hypoxia is frequent at induction and during maintenance of anesthesia and is commonly addressed once oxygen saturation fall below 85%.Blood transfusion practices vary widely variable among anesthesiologists and blood pressure is only a poor surrogate of tissue perfusion. Newer technologies, whereas acknowledging important limitations, may represent the currently best tools available to monitor tissue perfusion. Harmonization of pediatric anesthesia education and training, development of evidence-based practice guidelines, and provision of centralized care appear to be paramount as well as pediatric center referrals and international data collection networks. SUMMARY: The NECTARINE provided new insights into European neonatal anesthesia practice and subsequent morbidity and mortality.Maintenance of physiological homeostasis, optimization of oxygen delivery by avoiding the triad of hypotension, hypoxia, and anemia are the main factors to reduce morbidity and mortality. Underlying and preexisting conditions such as prematurity, congenital abnormalities carry high risk of morbidity and mortality and require specialist care in pediatric referral centers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".