PRIORITIZING PREVENTION AND CLOSING CARE GAPS: DEVELOPMENT OF A CANADIAN CARDIOMETABOLIC PREVENTION CLINIC
Bibliographic record
Abstract
Preventive Cardiology Best Practices Patients with dyslipidemia, obesity, diabetes, hypertension, and renal impairment are at high risk for cardiovascular diseases. Contemporary preventative strategies are complex. In Canada, care gaps exist in preventative medicine. We discuss the development of an academic intra-disciplinary cardiometabolic clinic. Its aim is to improve preventative care for patients and educate trainees and health care providers. A multidisciplinary advisory team was convened in Jan 2021. Infrastructure was created, including a clinic referral form, a patient satisfaction survey, a clinic database, and a web presence for patient education. Metrics include patient satisfaction, biometrics, laboratory values, test results, and medications. In the first 6 months, 95 patients have been referred to the cardiometabolic clinic, most through primary care. 12.6% had a statin indicated condition, and 14.7%, 24.2%, and 30.5% were at high, medium, and low cardiovascular risk based on the Framingham Risk Score. On the first visit, 62% had a statin added or their statin dose increased, and 78% have had their medications adjusted in some way. The use of high sensitivity troponin I (hsTnI) as a biomarker was high, with 64% of patients having hsTnI measured. 90% of patients were either “satisfied” or “very satisfied” with their appointment, and patients reported improvement in their understanding of their cardiovascular risk. Development of a novel cardiometabolic clinic in a Canadian environment is possible. Initial results are promising and shows improvement in evidence based medical therapy. Most patients had medications adjusted and high satisfaction with their visit. Ongoing evaluation will examine long term medication adherence. Development of a “learner's toolkit” for residents and fellows is underway.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".