Adherence to Guidelines: Experience of a Canadian Stroke Prevention Clinic
Bibliographic record
Abstract
BACKGROUND: Few studies have assessed the performance of stroke prevention clinics. In particular, limited information exists on patient compliance, achievement of therapeutic targets, and related occurrence of vascular events. METHODS: We compared our clinical practice to recommendations from published guidelines in newly referred patients for transient ischemic attack (TIA) or ischemic stroke between 2008 and 2010. We monitored our cohort for at least 1 year and assessed for adequacy of vascular risk factor management, drug adherence, and occurrence of nonlethal vascular outcomes. RESULTS: Of 408 patients, 57.8% had a stroke and 42.2% a TIA. The mean age was 68±13 years, and 52% male. Average follow-up was 15.8 months. During follow-up, 253 patients (70.3%) completely achieved their blood pressure target, 151 (45.5%) achieved their low-density lipoprotein (LDL) cholesterol target, and 407 (99.8%) were on antithrombotics. Eighty-nine patients (21.8%) attained optimal therapy status, defined as reaching targets for LDL cholesterol, blood pressure, and antithrombotic use. Adherence to drug therapy was associated with attainment of optimal therapy status (p=0.01). Diabetes was associated with lower probability of attaining optimal therapy status (odds ratio [OR], 0.36; 95% confidence interval [CI], 0.20-0.66) and blood pressure targets (OR, 0.09; 95% CI, 0.05-0.17). During follow-up, 52 (12.7%) patients had a nonlethal vascular event. CONCLUSION: Our study shows good attainment of therapeutic goals associated with adherence to drug therapy. However, optimal therapy status and blood pressure targets were more difficult to attain in patients with diabetes; therefore, more intensive preventive efforts may be required for these individuals.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".