Early cholecystectomy for acute cholecystitis: a population-based retrospective cohort study of variation in practice
Bibliographic record
Abstract
BACKGROUND: Despite evidence in favour of early cholecystectomy for most patients with acute cholecystitis, variation in practice has been reported across hospitals worldwide. We sought to characterize the extent and potential sources of variation in the performance of early cholecystectomy for acute cholecystitis within a large regional health care system. METHODS: We used a population-based retrospective cohort design. The cohort was limited to adults with a first episode of acute cholecystitis, admitted through the emergency department. Patients were identified using administrative databases comprising all emergency department visits and hospital admissions in Ontario from 2004 to 2010. Patient and hospital characteristics associated with early cholecystectomy (within 7 d of emergency department presentation) were identified using multilevel logistic regression. RESULTS: We identified 24 437 patients admitted to 106 hospitals with a first episode of acute cholecystitis. Most (58%, n = 14 286) underwent early cholecystectomy. Rates of early cholecystectomy varied widely across hospitals (median 51%, interquartile range [IQR] 25%-72%), even among healthy patients aged 18-49 years with uncomplicated cholecystitis (median 74%, IQR 41%-88%). Multivariable multilevel analysis showed that hospitals in the lowest quartile for volume of acute cholecystitis admissions had the lowest adjusted odds of early cholecystectomy (odds ratio 0.53, 95% confidence interval 0.35-0.78) and that hospital effects accounted for half (27%) of the explained variation (53%) in early cholecystectomy. INTERPRETATION: Across the hospitals of a regional health care system, similar patients with acute cholecystitis did not receive comparable care. Hospital-specific initiatives should be considered to facilitate early cholecystectomy for patients with acute cholecystitis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".