Case management for blood pressure and lipid level control after minor stroke: PREVENTION randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Optimization of systolic blood pressure and lipid levels are essential for secondary prevention after ischemic stroke, but there are substantial gaps in care, which could be addressed by nurse- or pharmacist-led care. We compared 2 types of case management (active prescribing by pharmacists or nurse-led screening and feedback to primary care physicians) in addition to usual care. METHODS: We performed a prospective randomized controlled trial involving adults with recent minor ischemic stroke or transient ischemic attack whose systolic blood pressure or lipid levels were above guideline targets. Participants in both groups had a monthly visit for 6 months with either a nurse or pharmacist. Nurses measured cardiovascular risk factors, counselled patients and faxed results to primary care physicians (active control). Pharmacists did all of the above as well as prescribed according to treatment algorithms (intervention). RESULTS: Most of the 279 study participants (mean age 67.6 yr, mean systolic blood pressure 134 mm Hg, mean low-density lipoprotein [LDL] cholesterol 3.23 mmol/L) were already receiving treatment at baseline (antihypertensives: 78.1%; statins: 84.6%), but none met guideline targets (systolic blood pressure ≤ 140 mm Hg, fasting LDL cholesterol ≤ 2.0 mmol/L). Substantial improvements were observed in both groups after 6 months: 43.4% of participants in the pharmacist case manager group met both systolic blood pressure and LDL guideline targets compared with 30.9% in the nurse-led group (12.5% absolute difference; number needed to treat = 8, p = 0.03). INTERPRETATION: Compared with nurse-led case management (risk factor evaluation, counselling and feedback to primary care providers), active case management by pharmacists substantially improved risk factor control at 6 months among patients who had experienced a stroke. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT00931788.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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 | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".