Chances of late surgery in relation to length of wait lists
Bibliographic record
Abstract
Background: The proportion of patients who undergo surgery within a clinically safe time is an important performance indicator in health systems that use wait lists to manage access to care. However, little is known about chances of on-time surgery according to variations in existing demand. We sought to determine what proportion of patients have had late coronary bypass surgery after registration on wait lists of different size in a network of hospitals with uniform standards for timing of surgery. Methods: Using records from a population-based registry, we studied wait-list times prospectively collected in a cohort of patients registered on wait lists for coronary artery bypass grafting procedures. We compared the number of weeks from registration to surgery against target access times established for three urgency groups. The chances of undergoing surgery within target time have been evaluated in relation to wait-list size at registration and the number of surgeries performed without registration on a wait list. Results: In 1991–2001, two in three patients were at risk of late surgery when registered on wait lists for isolated coronary bypass procedures in British Columbia, Canada. Although urgent patients had never seen a wait list with clearance time exceeding one week, the odds of on-time surgery were reduced by 25%, odds ratio [OR] = 0.75 (95% confidence interval [CI] 0.65–0.87) for every additional operation performed without registration on a list. When the wait list at registration required a clearance time of over one month, semi-urgent patients had 51% lower odds of on-time surgery as compared to lists with clearance time less than one week, OR = 0.49 (95%CI 0.41–0.60), after adjustment for age, sex, comorbidity, calendar period, hospital and week on the list. In the non-urgent group, the odds were 69% lower, OR = 0.31 (95%CI 0.20–0.47). Every time an operation in the same hospital was performed without registration on a wait list, the odds of on-time surgery for listed patients were reduced by 7%, OR = 0.93 (95%CI 0.91–0.95) in the semi-urgent group, and by 10%, OR = 0.90 (95%CI 0.87–0.94), in the non-urgent group. Conclusion: Chances of late surgery increase with the wait-list size for semi-urgent and non-urgent patients needing coronary bypass surgery. The weekly number of patients who move immediately from angiography to the operation without registration on a wait list reduced chances of surgery within target time in all urgency groups of listed patients. When advising patients who will be placed on the wait list about the expected time to treatment, hospital managers should take into account the current list size as well as the weekly number of patients who require CABG immediately after undergoing coronary angiography.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".