Factors influencing waiting times for elective laparoscopic cholecystectomy.
Bibliographic record
Abstract
INTRODUCTION: Health Canada states that waiting list information and management systems in Canada are woefully inadequate, especially for elective surgical procedures. Understanding the reasons for waiting is paramount to achieving fairness and equity. The objective of this study was to examine the impact of demographic and clinical factors and surgeon volume on waiting times for laparoscopic cholecystectomy (LC). METHODS: We comprehensively applied a wait-list database for all surgical procedures across a division of general surgery and performed a chart review of all patients undergoing LC in 2002 to collect additional demographic and clinical data. We excluded patients undergoing LC on an emergent basis or as a secondary procedure. For each patient, we calculated 2 time intervals: time from the receipt of consult to the surgical consult (interval A) and time from the surgical consult to the LC (interval B). Surgeons were categorized a priori into low- and high-volume groups, based on the median number of procedures they had performed. All analyses examining waiting times were performed with nonparametric methods. RESULTS: The study cohort included 294 patients; most (94.6%) underwent LC for biliary colic. The median waiting times for interval A and interval B were 22 days and 50 days, respectively. No associations were identified between any of the examined waiting times, sex, diagnosis or Charlston Comorbidity Index. High surgeon volume was associated with longer waiting times for interval A (median 26 v. 19 d; p=0.04) and interval B (median 58 v. 35 d; p=0.003) and was also associated with a greater number of episodes of biliary colic (2.7 v. 2.0; p=0.03). CONCLUSION: There is significant variability in specific waiting times for LC, which appears to be associated with surgeon volume. Better prioritization of patients undergoing nonemergent LC is required to improve patient care.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".