An Assessment of Specialist Physician Referral Practices for Long-Term Cardiovascular Risk Reduction in the Community: Are We Using Our Available Resources?
Bibliographic record
Abstract
Background: Our aim was to evaluate specialist physicians’ referral patterns for cardiovascular risk reduction (CRR) while identifying existing CRR programs in a large Canadian city. Methods: This was a cross-sectional study involving an electronic survey of cardiologists and internists in Calgary, Alberta, to assess CRR referral patterns. A concurrent online search for programs addressing CRR was undertaken. Results: Twenty-four CRR programs were identified. Nine (37.5%, 95% CI: 21.2–57.2) required physician referral. Half (50.0%, 95% CI: 31.4–68.6) had no direct patient cost. A majority of surveyed physicians estimated that more than half of their patients have at least one modifiable risk factor. However, 75.0% (95% CI: 61.2–85.1) had referred less than half of these patients for CRR. Conclusion: Our study demonstrates a gap in specialist physician referral practices for CRR. Patients with modifiable risk factors may not be accessing valuable CRR resources.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".