Cardiac Investigation Trends Among Primary Care Physicians: A Paradigm Shift
Bibliographic record
Abstract
Objective While numerous recent guidelines support coronary computed tomography angiography (CTA) as a first-line test for stable chest pain, it remains underutilized by primary care physicians (PCPs). We aimed to evaluate cardiac investigation ordering practices following education sessions, as well as the total number of downstream tests and time to diagnosis for patients presenting with stable chest pain. Methods A retrospective chart review was completed for eligible patients assessed at the Women's College Hospital Family Practice Health Centre between 2017 and 2019 following the education sessions. The outcome measures were first-choice cardiac investigation, additional downstream testing, time from presentation to first investigation, and time to final diagnosis. Results 419 patients were included in the final analysis (74.70% female; mean age 61 ± 11 years). Coronary CTA requests by PCPs increased between 2017 and 2019 (18 vs 72 tests; P < .0001). When coronary CTA was the first-line test, patients were less likely to receive additional downstream testing when compared to those receiving other first-line investigations ( P < .0001). Coronary CTA was associated with longer time to diagnosis than stress echocardiography (47 ± 45 vs 27 ± 36 days; P = .0068) due to limited availability of coronary CTA appointment times. There was no significant difference in time to final diagnosis among the cardiac imaging modalities observed in the cohort ( P = .0623). Conclusion Utilization of coronary CTA as the first-line test for stable chest pain increased following our education sessions targeting PCPs. Coronary CTA was associated with less downstream testing compared to other non-invasive cardiac investigations.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".