Defining awakening from anesthesia in neonates: a consensus study
Bibliographic record
Abstract
BACKGROUND: A descriptive tool for determining awakening in infants is desirable to test the value of depth of anesthesia monitors. Although scales and criteria have been developed for children and infants, none has been applied to the study of anesthetised neonates. We aimed to seek consensus in a group of experts on a definition of awakening at the end of anesthesia in neonates. METHODS: We used a modified Delphi technique with an iterative process of questionnaires and anonymised feedback. Communication was conducted by email. Thirty-one consultant pediatric anesthetists in the UK and Ireland took part. Consensus was defined a priori as 80% agreement. RESULTS: The 83% of respondents agreed that defining awakening is possible. Consensus was reached on six criteria and also that a combination of these criteria must be used. As crying and attempting to cry are similar, we propose that at least two of the following five behaviors are present to consider a neonate awake after anesthesia: (i) crying or attempting to cry, (ii) vigorous limb movements, (iii) gagging on a tracheal tube, (iv) eyes open, and (v) looking around. There was also consensus that three stimuli are appropriate to test rousability in neonates awakening from anesthesia: (i) removal of skin adhesive tape, (ii) stroking/tickling the skin or gentle shaking, and (iii) pharyngeal suction. CONCLUSIONS: We propose a scale for determining awakening from anesthesia in neonates that may be used in future studies, particularly regarding electroencephalographic data and depth of anesthesia monitoring in neonates.
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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 | 0.211 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| 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".