Abstract P-470: EVALUATION OF IATROGENIC WITHDRAWAL FROM OPIOIDS AND BENZODIAZEPINES IN THE PEDIATRIC INTENSIVE CARE UNIT
Bibliographic record
Abstract
Aims & Objectives: Iatrogenic withdrawal syndrome (IWS) is common in pediatric intensive care unit (PICU). Main objective of this study was to assess the incidence of IWS in the PICU at our center (academic children’s hospital). Secondary objective was to identify risk factors of IWS. Methods This is a descriptive and longitudinal retrospective study, including children admitted to our PICU between April 2015 and February 2017 who received opioids and/or benzodiazepines for more than 72 hours. The presence of IWS was assessed with the Withdrawal Assessment Tool-1 (WAT-1) or Neonatal Abstinence Score (NAS). Results 73 patients were included (median age of 8.7 weeks (4.4; 58.1)). The incidence of IWS in our population was 34.2% (25/73). Sedative/analgesic agents were used for an average of 160.8 hours (± 78.7). Main agents used were midazolam and morphine continuous infusions (68/73 (93.2%) and 49/73 (67.1%), respectively). Patients received a median of 0.52 mg/kg/day (0.35; 0.81) IV morphine equivalent and 0.36 mg/kg/day (0.25; 0.61) midazolam equivalent. IWS was more common in boys (OR: 4.4, 95% CI 1.4; 13.5), even after adjustment (OR: 5.4; 95% CI, 1.3–21.8). History of prematurity was also associated with increased risk of IWS (adjusted OR: 5.4; 95% CI, 1.1–26.6). There was no difference between the IWS and no-IWS groups for age, Pediatric Risk of Mortality (PRISM) score, total opioids and benzodiazepines doses and PICU length of stay. Conclusions IWS was seen in several patients admitted to PICU. This is more common among young boys and those with a history of prematurity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".