Emergence delirium, pain or both? a challenge for clinicians
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Children commonly display early postoperative negative behavior (e-PONB) after general anesthesia, which includes emergence delirium (ED), discomfort, temperament, and pain. However, it is often difficult for the caregiver to discriminate between various aspects of e-PONB. OBJECTIVE: This prospective observational study evaluates the possibility to distinguish between ED and pain in young children using validated pediatric observational scales in the early postoperative phase. METHODS: Following institutional approval and written consent, children undergoing elective adenoidectomy and/or tonsillectomy were enrolled. Following standardized anesthesia, two trained observers simultaneously evaluated children's behavior with the Paediatric Anaesthesia Emergence Delirium Scale (PAED) and with the Face, Legs, Activity, Cry, Consolability scale (FLACC) at extubation, and at 5, 10, and 15 min. RESULTS: Of 150 children that completed the study, 32 (21%) had ED, 7 (5%) had pain, and 98 (65%) had simultaneously both ED and pain. The association of 'No eye contact', 'No purposeful action' and 'No awareness of surroundings' (ED1) had a sensitivity of 0.96 and a specificity of 0.80 (PPV 0.97, NPV 0.78) to identify ED. 'Inconsolability' and 'Restlessness' (ED2) had a sensitivity of 0.69 and a specificity of 0.88 (PPV 0.83 and NPV 0.78) to identify pain. CONCLUSION: It is difficult to differentiate between ED and pain using FLACC and PAED scores. 'No eye contact', 'No purposeful action', and 'No awareness of surroundings' significantly correlated with ED. 'Inconsolability' and 'Restlessness' are not reliable enough to identify pain or ED in the first 15 min after awakening.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it