Long‐Term Impact of a Community Pharmacist Intervention on Cholesterol Levels in Patients at High Risk for Cardiovascular Events: Extended Follow‐up of the Second Study of Cardiovascular Risk Intervention by Pharmacists (SCRIP‐<i>plus</i>)
Bibliographic record
Abstract
STUDY OBJECTIVE: To determine the effect of a community pharmacist intervention in patients at high risk for coronary heart disease on low-density lipoprotein cholesterol (LDL) levels 1 year after completion of the Second Study of Cardiovascular Risk Intervention by Pharmacists (SCRIP- plus ). METHODS: Patients who completed the original study were invited to make a single return visit to their community pharmacy so that the pharmacist could measure their fasting LDL level using a point-of-care device. The primary outcome was change in LDL level from the 6-month (final) visit to the extended follow-up evaluation. RESULTS: Of the 359 patients who completed the original 6-month visit, data were collected for 162 (45%) patients. The mean +/- SD LDL level at completion of the original study was 107.9 +/- 33.6 mg/dl (2.79 +/- 0.96 mmol/L) (an increase of 2.7 mg/dl [0.07 mmol/L], 95% confidence interval -19.3-7.3 [-0.5-0.19]). Sixty-one (38%) patients were at the target LDL level (< 96.7 mg/dl [< 2.50 mmol/L]). CONCLUSION: The LDL reduction was maintained 1 year after completion of the extended follow-up. Since most patients were still not at the target LDL level, this finding suggests that continuing intervention is necessary to help patients reach this target.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".