A Quality Improvement Initiative to Improve Early Postoperative Pain Outcomes After Tonsillectomy in Children
Bibliographic record
Abstract
BACKGROUND: Intraoperative opioid use during pediatric tonsillectomy is commonly avoided to reduce the risk of postoperative respiratory adverse events (PRAEs). Avoidance of perioperative opioids may contribute to increased early postoperative pain, which can result in patients receiving rescue doses of opioids in the postanesthesia care unit (PACU). AIMS: The aim of this project was to reduce moderate to severe pain in PACU for pediatric tonsillectomy/adenotonsillectomy patients by 50% within 12 months. METHODS: Pilot data was collected on the intraoperative care and PACU pain outcomes of patients between June 2018 and June 2020. A six-item toolkit was designed, then implemented from April 2021, with identical data points collected for comparison. Postintervention patients were categorized into toolkit compliance groups: (1) Standard of care (< 5 of 6 items delivered), (2) Partial Toolkit (5 of 6 items delivered), and (3) Toolkit (100% adherence). Statistical process control charts were used for data analysis. RESULTS: Data was collected for 420 patients. Baseline data reported 65.8% of patients experienced moderate-severe pain in PACU. In the first 12 months of toolkit implementation (2021-2022), the incidence of moderate to severe pain decreased to 47.1% in the 100% adherence group (28% reduction). In subsequent years (2022-2024), this measure decreased further to 31% (53% reduction overall). Pretoolkit, 69% of patients received rescue opioids in PACU. In the first 12 months of toolkit implementation (2021-2022), the incidence of rescue opioids in PACU decreased to 35% in the 100% toolkit adherence group (49% reduction). From 2022 to 2024, this decreased to 45% (35% reduction). CONCLUSION: Through the implementation of the tonsillectomy toolkit, we helped reduce early postoperative pain by 28% in the first year. Continued data collection showed the intervention to be sustainable and delivered subsequent decreases in moderate to severe pain by a factor of 53%. These improvements were achieved without increasing PRAEs, postoperative nausea/vomiting incidence, or PACU length of stay.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".