The Impact of a Stroke Prevention Clinic in Diagnosing Modifiable Risk Factors for Stroke
Bibliographic record
Abstract
OBJECTIVE: To evaluate the referral patterns of patients to a stroke prevention clinic (SPC) and to test the adequacy of prereferral diagnosis and management of modifiable risk factors for stroke. METHODS: We collected prospective data on consecutive patients referred to the SPC at University of Alberta Hospital in Edmonton, Alberta, Canada. Outcome measures included: alternate diagnoses to stroke or transient ischemic attack (TIA), uncontrolled or undiagnosed hypertension, hyperlipidemia and diabetes, therapies, and investigations leading to carotid endarterectomy. RESULTS: Two thousand and eleven patients were referred to SPC. Nearly 25% of the referrals originated from the emergency room and the rest from general physicians. Of the referrals, 68.7% were confirmed as TIA or stroke at the SPC. Among 1381 patients with TIA or stroke, 736 had history of hypertension. Uncontrolled hypertension was found in 265 patients (36.0% of those with hypertension: 95% CI: 32.5-39.5) while undiagnosed hypertension was found in 103 (15.9% of those without hypertension: 95%CI: 13.14-18.79). History of hyperlipidemia was present in 451 patients (32.6%) and 356 (78.9%: 95% CI: 75.2-82.69) of these patients were not at target for secondary prevention. Among 930 patients without history of hyperlipidemia, 739 (79.5%: 95% CI: 76.8-82.1) were diagnosed with hyperlipidemia through the SPC. Fasting blood glucose levels above 7.1 mmol/L in patients with and without history of diabetes were 221 (79.2%: 95% CI: 74.5-83.9) and 66 (6%: 95%CI: 4.6-7.4) respectively. CONCLUSIONS: Management of risk factors for stroke needs improvement. SPCs should consider actively managing the classical modifiable risk factors of stroke.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".