Abstract 5147: A Cluster Randomised Controlled Trial to Evaluate an Ambulatory Primary Care Management Program for Patients with Dyslipidemia: TEAM Study
Bibliographic record
Abstract
Dyslipidemia management in primary care is often sub-optimal. New Quebec (Canada) legislation increases the potential for family physician-community pharmacist collaborative care (PPCC). We hypothesized that PPCC can improve the management of dyslipidemia. In a randomized controlled trial, clusters of physicians and pharmacists within the same geographical area were randomly assigned to the PPCC or the usual care (UC) group. PPCC pharmacists attended a one-day workshop and successfully completed a clinical skills evaluation. In the PPCC intervention, the physicians are responsible for the diagnosis and the prescription of lipid-lowering pharmacotherapy, while the pharmacists initiate treatment; request appropriate laboratory tests; monitor effectiveness, safety, and adherence to treatment; and adjust medication dosage accordingly. In PPCC, physician-pharmacist communication procedures were pre defined to enhance professional collaboration. Moderate and high CHD risk patients initiating statin treatment or currently on treatment but inadequately controlled were recruited and followed-up for 1 year. The primary endpoint was the mean change difference in LDL between groups after 1 year. Secondary endpoints included the proportion of patients at or below their target LDL and TC/HDL ratio, as defined by the Canadian Lipid Guidelines, after 1 year. Multilevel and multivariate regression models were used. We enrolled 15 clusters of physicians and pharmacists (PPCC: 108 patients; UC: 117 patients). The mean change in LDL after 1 year was -1.11 mmol/L (95%CI: −1.26 to −0.96) and −0.89 mmol/L (−0.95 to −0.83) in PPCC and UC groups, respectively. The adjusted mean change difference in LDL between PPCC and UC groups was −0.11 mmol/L (−0.34 to 0.11). After 1 year, 80.6% of PPCC patients and 72.9% of UC patients were at or below their target lipid levels (adjusted OR 2.1; 1.2 to 3.8; p=0.008). PPCC patients had more lipid-lowering pharmacotherapy changes (OR: 2.0; 1.1 to 3.8) and were more likely to report lifestyle changes (OR: 3.9; 2.4 to 6.3). Our data shows that the Family physician-community pharmacist collaborative care improves the adherence to the Canadian Lipid Guidelines.( ISRCTN66345533 )
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".