Adding pharmacists to primary care teams reduces predicted long‐term risk of cardiovascular events in Type 2 diabetic patients without established cardiovascular disease: results from a randomized trial
Bibliographic record
Abstract
AIM: To determine the impact of adding pharmacists to primary care teams on predicted 10-year risk of cardiovascular events in patients with Type 2 diabetes without established cardiovascular disease. METHODS: This was a pre-specified secondary analysis of randomized trial data. The main study found that, compared with usual care, addition of a pharmacist resulted in improvements in blood pressure, dyslipidaemia, and hyperglycaemia for primary care patients with Type 2 diabetes. In this sub-study, predicted 10-year risk of cardiovascular events at baseline and 1 year were calculated for patients free of cardiovascular disease at enrolment. The primary outcome was change in UK Prospective Diabetes Study (UKPDS) risk score; change in Framingham risk score was a secondary outcome. RESULTS: Baseline characteristics were similar between the 102 intervention patients and 93 control subjects: 59% women, median (interquartile range) age 57 (50-64) years, diabetes duration 3 (1-6.5) years, systolic blood pressure 128 (120-140) mmHg, total cholesterol 4.34 (3.75-5.04) mmol/l and HbA(1c) 54 mmol/mol (48-64 mmol/mol) [7.1% (6.5-8.0%)]. Median baseline UKPDS risk score was 10.2% (6.0-16.7%) for intervention patients and 9.5% (5.8-15.1%) for control subjects (P = 0.80). One-year post-randomization, the median absolute reduction in UKPDS risk score was 1.0% greater for intervention patients compared with control subjects (P = 0.032). Similar changes were seen with the Framingham risk score (median reduction 1.2% greater for intervention patients compared with control subjects, P = 0.048). The two risk scores were highly correlated (rho = 0.83; P < 0.001). CONCLUSION: Adding pharmacists to primary care teams for 1 year significantly reduced the predicted 10-year risk of cardiovascular events for patients with Type 2 diabetes without established cardiovascular disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".