Do case‐generic measures of queue performance for bypass surgery accurately reflect the waiting‐list experiences of those most urgent?
Bibliographic record
Abstract
BACKGROUND: Queue performance is typically assessed using generic measures, which capture the queue in aggregate. The objective of this study was to examine whether case-generic measures of queue performance appropriately reflected the waiting-list experiences of those patients with greatest disease severity. METHODS: We examined the queue for isolated coronary artery bypass grafting (CABG) in Ontario between April 1993 and March 2000 using data obtained from the Cardiac Care Network. Our primary measure of queue performance was the proportion of patients who received their bypass surgery within their recommended maximum waiting times (%RMWTs) in any given month. We compared case-generic measures of queue performance to case-specific measures of queue performance stratified by urgency level. RESULTS: The queue was largely comprised of elective cases ranging from 73% (1993) to 57%(1999). Urgent patients comprised the minority of the queue ranging from 14% (1993) to 20% (1999). Case-generic month-to-month variations in the percentage of cases completed within RMWTs (an aggregated waiting list measure encompassing the characteristics of all patients in the queue) closely resembled the experiences of elective patients (R2 = 0.81), but conversely, bore little relationship to the waiting-list experiences of those most urgent (R2 = 0.15). INTERPRETATION: Case-generic measures of queue performance for bypass surgery in Ontario were not reflective of the waiting-list experiences of those most urgent. Our results reinforce the concept that urgency-specific waiting list monitoring systems are required to best evaluate and appropriately respond to fluctuations in queue performance.
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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 | 0.052 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".