Collaborative Cardiovascular Risk Reduction in Primary Care II (CCARP II)
Bibliographic record
Abstract
BACKGROUND: Previous pharmacist interventions to reduce cardiovascular (CV) risk have been limited by low patient enrolment. The primary aim of this study was to implement a collaborative pharmacist intervention that used a systematic case-finding procedure to identify and manage patients with uncontrolled CV risk factors. METHODS: This was an uncontrolled, program implementation study. We implemented a collaborative pharmacist intervention in a primary care clinic. All adults presenting for an appointment with a participating physician were systematically screened and assessed for CV risk factor control by the pharmacist. Recommendations for risk factor management were communicated on a standardized form, and the level of pharmacist follow-up was determined on a case-by-case basis. We recorded the proportion of adults exhibiting a moderate to high Framingham risk score and at least 1 uncontrolled risk factor. In addition, we assessed before-after changes in CV risk factors. RESULTS: Of the 566 patients who were screened prior to visiting a participating physician, 186 (32.9%) exhibited moderate or high CV risk along with at least 1 uncontrolled risk factor. Physicians requested pharmacist follow-up for 60.8% (113/186) of these patients. Of the patients receiving the pharmacist intervention, 65.5% (74/113) were at least 50% closer to 1 or more of their risk factor targets by the end of the study period. Significant risk factor improvements from baseline were also observed. DISCUSSION: Through implementation of a systematic case-finding approach that was carried out by the pharmacist on behalf of the clinic team, a large number of patients with uncontrolled risk factors were identified, assessed and managed with a collaborative intervention. CONCLUSION: Systematic case finding appears to be an important part of a successful intervention to identify and manage individuals exhibiting uncontrolled CV risk factors in a primary care setting.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".