Patient, physician and geographic predictors of cardiac stress testing strategy in Ontario, Canada: a population-based study
Bibliographic record
Abstract
OBJECTIVES: To identify patient, physician and geographic level factors that are associated with variation in initial stress testing strategy in patients evaluated for chest pain. DESIGN: Retrospective cohort study. SETTING: Population-based study of patients undergoing evaluation for chest pain in Ontario, Canada between 1 January 2011 and 31 March 2018. PARTICIPANTS: 103 368 patients who underwent stress testing (graded exercise stress testing (GXT), stress echocardiography (stress echo) or myocardial perfusion imaging (MPI)) following evaluation for chest pain. PRIMARY AND SECONDARY OUTCOME MEASURES: To identify the patient, physician and geographic level factors associated with variation in initial test selection, we fit two separate 2-level hierarchical multinomial logistic regression models for which the outcome was initial stress testing strategy (GXT, MPI or stress echo). RESULTS: There was significant variability in the initial type of stress test performed, with approximately 50% receiving a GXT compared with approximately 36% who received MPI and 14% who received a stress echo. Physician-level factors were key drivers of this variation, accounting for up to 59.0% of the variation in initial testing. Physicians who graduated medical school >30 years ago were approximately 45% more likely to order an initial stress echo (OR 1.45, 95% CI 1.17 to 1.80) than a GXT. Cardiovascular disease specialists were approximately sevenfold more likely to order an initial MPI (OR 7.35, 95% CI 5.38 to 10.03) than a GXT. Patients aged >70 years were approximately fivefold more likely to receive an MPI (OR 4.74, 95% CI 4.42 to 5.08) and approximately 26% more likely to receive a stress echo (OR 1.26, 95% CI 1.15 to 1.38) than a GXT. CONCLUSIONS: We report significant variability in initial stress testing strategy in Ontario. Much of that variability was driven by physician-level factors that could potentially be addressed through educational campaigns geared at reducing this variability and improving guideline adherence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".