A Multicenter Collaborative to Improve Postoperative Pain Management in the NICU
Bibliographic record
Abstract
OBJECTIVES: This quality improvement initiative aimed to decrease unrelieved postoperative pain and improve family satisfaction with pain management. METHODS: NICUs within the Children's Hospitals Neonatal Consortium that care for infants with complex surgical problems participated in this collaborative. Each of these centers formed multidisciplinary teams to develop aims, interventions, and measurement strategies to test in multiple Plan-Do-Study-Act cycles. Centers were encouraged to adopt evidence-based interventions from the Clinical Practice Recommendations, which included pain assessment tools, pain score documentation, nonpharmacologic treatment measures, pain management guidelines, communication of a pain treatment plan, routine discussion of pain scores during team rounds, and parental involvement in pain management. Teams submitted data on a minimum of 10 surgeries per month, spanning from January to July 2019 (baseline), August 2019 to June 2021 (improvement work period), and July 2021 to December 2021 (sustain period). RESULTS: The percentage of patients with unrelieved pain in the 24-hour postoperative period decreased by 35% from 19.5% to 12.6%. Family satisfaction with pain management measured on a 3-point Likert scale with positive responses ≥2 increased from 93% to 96%. Compliance with appropriate pain assessment and numeric documentation of postoperative pain scores according to local NICU policy increased from 53% to 66%. The balancing measure of the percentage of patients with any consecutive sedation scores showed a decrease from 20.8% at baseline to 13.3%. All improvements were maintained during the sustain period. CONCLUSIONS: Standardization of pain management and workflow in the postoperative period across disciplines can improve pain control in infants.
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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 | 0.043 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".