Implementation and Case-Study Results of Potentially Better Practices to Improve Pain Management of Neonates
Bibliographic record
Abstract
OBJECTIVE: Collaborative quality improvement techniques were used to facilitate local quality improvement in the management of pain in infants. Several case studies are presented to highlight this process. METHODS: Twelve NICUs in the Neonatal Intensive Care Quality Improvement Collaborative 2002 focused on improving neonatal pain management and sedation practices. These centers developed and implemented evidence-based potentially better practices for pain management and sedation in neonates. The group introduced changes through plan-do-study-act cycles and tracked performance measures throughout the process. RESULTS: Strategies for implementing potentially better practices varied between centers on the basis of local characteristics. Individual centers identified barriers to implementation, developed tools for improvement, and shared their experience with the collaborative. Baseline data from the 12 sites revealed substantial opportunities for improved pain management, and local potentially better practice implementation resulted in measurable improvements in pain management at participating centers. CONCLUSIONS: The use of collaborative quality improvement techniques enhanced local quality improvement efforts and resulted in effective implementation of potentially better practices at participating 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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".