Early Cholecystectomy for Acute Cholecystitis Offers the Best Outcomes at the Least Cost: A Model-Based Cost-Utility Analysis
Bibliographic record
Abstract
BACKGROUND: The application of early cholecystectomy for acute cholecystitis remains inconsistent across hospitals worldwide. Given the constrained nature of health care spending, careful consideration of costs relative to the clinical consequences of alternative treatments should support decision making. We present a cost-utility analysis comparing alternative time frames of cholecystectomy for acute cholecystitis. STUDY DESIGN: A Markov model with a 5-year time horizon was developed to compare costs and quality-adjusted life-years (QALY) gained from 3 alternative management strategies for the treatment of acute cholecystitis: early cholecystectomy (within 7 days of presentation), delayed elective cholecystectomy (8 to 12 weeks from presentation), and watchful waiting, where cholecystectomy is performed urgently only if recurrent symptoms arise. Model inputs were selected to reflect patients with uncomplicated acute cholecystitis-without concurrent common bile duct obstruction, pancreatitis, or severe sepsis. Real-world outcome probability and cost estimates included in the model were derived from analysis of population-based administrative databases for the province of Ontario, Canada. The QALY values were derived from utilities identified in published literature. Parameter uncertainty was evaluated through probabilistic sensitivity analyses. RESULTS: Early cholecystectomy was less costly (C$6,905 per person) and more effective (4.20 QALYs per person) than delayed cholecystectomy (C$8,511; 4.18 QALYs per person) or watchful waiting (C$7,274; 3.99 QALYs per person). Probabilistic sensitivity analysis showed early cholecystectomy was the preferred management in 72% of model iterations, given a cost-effectiveness threshold of C$50,000 per QALY. CONCLUSIONS: This cost-utility analysis suggests early cholecystectomy is the optimal management of uncomplicated acute cholecystitis. Furthermore, deferring surgery until recurrent symptoms arise is associated with the worst clinical outcomes.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".