The evaluation of a formalized queue management system for coronary angiography waiting lists.
Bibliographic record
Abstract
BACKGROUND: Lengthy waiting lists for coronary angiography have been described in many health care systems worldwide. The extent to which formal queue management systems may improve the prioritization and survival of patients in the angiography queue is unknown. OBJECTIVE: To prospectively evaluate the performance of a formal queue management system for patients awaiting coronary angiography in Ontario. METHODS: The coronary angiography urgency scale, a formal queue management system developed in 1993 using a modified Delphi panel, allocates recommended maximum waiting times (RMWTs) in accordance with clinical necessity. By using a provincial clinical registry, 35,617 consecutive patients referred into the coronary angiography queue between April 1, 2001, and March 31, 2002, were prospectively tracked. Cox proportional hazards models were used to examined mortality risk across urgency after adjusting for additional clinical and comorbid factors. RESULTS: Good agreement was determined in urgency ratings between scores from the coronary angiography urgency scale and implicit physician judgement, which was obtained independently at the time of the index referral (weighted kappa = 0.49). The overall mortality in the queue was 0.3% (0.47%, 0.26% and 0.13% for urgent, semiurgent and elective patients, respectively). Urgency, as specified by the coronary angiography urgency scale, was the strongest predictor of death in the queue (P<0.001). However, when patients were censored according to their RMWTs, mortality was similar across different levels of urgency. Consequently, up to 18.5 deaths per 10,000 patients could have potentially been averted had patients been triaged and undergone coronary angiography within the RMWT as specified by the coronary angiography urgency scale. CONCLUSIONS: The incorporation of the coronary angiography urgency scale as a formal queue management system may decrease mortality in the coronary angiography queue. The authors recommend its implementation in health care systems where patients experience excessive waiting time delays for 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 | 0.006 | 0.000 |
| 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".