Routine Use of Analgesia for Venipuncture in a Tertiary Level Neonatal Intensive Care Setting: A Quality Improvement Study
Bibliographic record
Abstract
Introduction: Neonatal exposure to pain can lead to altered pain perception in later years of life. Despite the availability of measures to alleviate pain, routine use is lacking. We decided to conduct a quality improvement (QI) study to increase the use of analgesia during venipuncture, a common procedure in neonatal intensive care units, from a baseline of 0% to 50% over 8 weeks. Methods: Fishbone analysis was used to identify the potential barriers, which were targeted to bring improvement through Plan-Do-Study-Action (PDSA) cycles. In the first cycle, education and training of healthcare providers were conducted for 3 weeks, followed by the second cycle, wherein the mother's own milk was made available bedside for analgesia use. In the third cycle, a small amount of pasteurized donor human milk was kept separately for analgesia, and 25% dextrose was made available in the fourth cycle as a last resort. The 2nd-4th PDSA cycles were performed for a period of 2 weeks each. Results: The use of analgesia improved to 26% from baseline after the first cycle and subsequently to 46%, 50%, and 53% after the second, third, and fourth cycles, respectively. During the sustenance phase, in the initial 2 months, there was a decrease in analgesia use, but with prompt interventions and timely remediation, it increased up to 60%, which was sustained for the subsequent 3 months. Conclusion: Using the QI model, we were able to identify lacunae in current care and drive a culture change, leading to an increase in the use of analgesia during venipuncture.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".