Quality Assurance and Performance Improvement Project for Suspected Appendicitis
Bibliographic record
Abstract
INTRODUCTION: Considerable variability exists in the diagnosis and management of acute appendicitis, affecting both quality and costs of care. This prospective cohort study aimed to decrease unnecessary radiological investigations, standardize radiological imaging, avoid unnecessary hospital admissions, and decrease our institution rate of negative appendectomy. METHODS: A multidisciplinary appendicitis care pathway was implemented. This pathway involved the use of the Pediatric Appendicitis Score, standardization of ultrasound reporting, and risk stratification to determine patient disposition. Patients were prospectively enrolled in the pathway and compared a preimplementation retrospective cohort. RESULTS: We included 235 patients in this study that took place between February 2017 and January 2018. An 88.5% pathway adherence rate for appropriate referral for ultrasounds, an 84% compliance rate for correct risk stratification, and the need for a surgical consult were achieved. After implementation, standardization of ultrasound (U/S) reporting increased from 0% to 78%. The rate of computed tomography utilization decreased from 7.3% to 4.7%. An appendectomy was completed in 68 (29%) of patients. There was only 1 (1.5%) negative appendectomy, compared to the prepathway institutional negative appendectomy rate of 4%. CONCLUSION: The implementation of a standardized, evidence-based, appendicitis care pathway has the potential to improve quality of care by reducing negative appendectomies, unnecessary computed tomography scans, and unnecessary hospital admissions. The participation of the emergency and diagnostic imaging departments is critical to the successful implementation of this quality improvement measure. This simple, effective model can be easily implemented at other centers to improve the care of children.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".