Impact of clinical urgency, physician supply and procedural capacity on regional variations in wait times for coronary angiography
Bibliographic record
Abstract
BACKGROUND: Despite universal health care, there continues to be regional access disparities to coronary angiography in Canada. Our objective was to evaluate the extent to which demand-side factors such as clinical urgency/need, and supply-side factors, as reflected by differences in physician and procedural supply account for these inequalities. METHODS: Our cohort consisted of 74,254 consecutive patients referred for coronary angiography in Ontario, Canada between April 1st 2005 and March 31st 2006, divided into three urgency strata based on a clinical urgency scale. Cox-proportional hazard models were developed, adjusting for age, gender, socioeconomic status (SES), region, and urgency score, with greater hazard ratios (HR) indicating shorter wait times. To evaluate mediators of any residual wait-time differences, we examined the influence of the regional supply of cath lab facilities, invasive cardiologists and general practitioners (GP). RESULTS: We found that the urgency score was a significant predictor of wait time in all three strata (urgent patients: HR 1.61 for each unit increase in patient urgency (95% Confidence interval (CI) 1.55-1.67); semi-urgent patients: HR 1.55 (95% CI 1.44-1.68); elective patients: HR 1.13 (95% CI 1.08-1.18)). After accounting for clinical need/urgency, regional wait time differences persisted; these were most consistently associated with variation in cath lab supply. The impact of invasive cardiologist supply was restricted to urgent patients while that of GP supply was confined to semi-urgent and elective patients. CONCLUSION: We found that there remained significant regional disparities in access to coronary angiography after accounting for clinical need. These disparities are partially explained by variations in supply of both procedural capacity and physician services, most notably in elective and semi-urgent patients.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".