Triage for coronary artery bypass graft surgery in Canada: Do patients agree on who should come first?
Bibliographic record
Abstract
BACKGROUND: The extent to which clinical and non-clinical factors impact on the waiting-list prioritization preferences of patients in the queue is unknown. Using a series of hypothetical scenarios, the objective of this study was to examine the extent to which clinical and non-clinical factors impacted on how patients would prioritize others relative to themselves in the coronary artery bypass surgical queue. METHODS: Ninety-one consecutive eligible patients awaiting coronary artery bypass grafting surgery at Sunnybrook Health Sciences Centre (median waiting-time duration prior to survey of 8 weeks) were given a self-administered survey consisting of nine scenarios in which clinical and non-clinical characteristic profiles of hypothetical patients (also awaiting coronary artery bypass surgery) were varied. For each scenario, patients were asked where in the queue such hypothetical patients should be placed relative to themselves. RESULTS: The eligible response rate was 65% (59/91). Most respondents put themselves marginally ahead of a hypothetical patient with identical clinical and non-clinical characteristics as themselves. There was a strong tendency for respondents to place patients of higher clinical acuity ahead of themselves in the queue (P < 0.0001). Social independence among young individuals was a positively valued attribute (P < 0.0001), but neither age per se nor financial status, directly impacted on patients waiting-list priority preferences. CONCLUSION: While patient perceptions generally reaffirmed a bypass surgical triage process based on principals of equity and clinical acuity, the valuation of social independence may justify further debate with regard to the inclusion of non-clinical factors in waiting-list prioritization management systems in Canada, as elsewhere.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 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) | 0.003 | 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".