How Well Are Hypertension, Hyperlipidemia, Diabetes, and Smoking Managed After a Stroke or Transient Ischemic Attack?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Stroke prevention clinics (SPCs) are not usually involved with the active management of hypertension, hyperlipidemia, diabetes, and smoking. The effect of consultations generated at SPCs on the adequacy of the management of these risk factors for stroke has not been well described, and few studies have long-term follow-up. METHODS: We performed a prospective study of 119 consecutive patients referred to an SPC for secondary prevention. One year after their baseline visit, patients were re-evaluated for the adequacy of the management of the above risk factors, and the proportion of improvement was assessed. RESULTS: One-hundred twelve patients returned for their 1-year follow-up visit. Sixty-six were male, and the average age was 65 years. Hypertension was present in 83 patients, hyperlipidemia in 92, diabetes in 26, and smoking in 38, and 80 had multiple risk factors. At baseline, 66% of patients with hypertension, 17% of patients with hyperlipidemia, and 23% of diabetics had adequate management of their respective risk factors. During 1 year of follow-up, hypertension management improved 20% (P<0.001) and lipid management improved 32% (P<0.001). There was no significant improvement in diabetes management or smoking cessation. CONCLUSIONS: Although our understanding of the benefit of addressing hypertension, hyperlipidemia, diabetes, and smoking for secondary prevention of stroke is evolving, we found marked room for improvement in the management of these four risk factors. SPCs may need to be more actively involved in the management of these modifiable risk factors, if we are to significantly impact the risk of recurrent stroke.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".