Risk Factors of Stroke in Pakistan: A Dedicated Stroke Clinic Experience
Bibliographic record
Abstract
BACKGROUND: Secondary prevention of cerebrovascular disease through dedicated stroke clinics has been shown to decrease recurrent vascular events in patients. However, there is limited literature describing such stroke clinic experiences from low and middle income countries. This study describes patient characteristics and observations made at the first systematized stroke clinic in Pakistan. METHODS: A retrospective audit of medical records of all patients presenting between September 2006 and August 2008 with a cerebrovascular event was conducted. Information about clinical presentation, modifiable risk factors and laboratory and radiological investigations was collected. Burden of disability was assessed using Modified Rankin score. Data was entered and analyzed using SPSS 14.0. RESULTS: 159 patients with a mean age of 57.0 +/- 13.9 years were included in this study and 34.6% of all patients were women. 108 patients were diagnosed with ischemic stroke (67.9%) while 34 patients presented with hemorrhagic stroke (21.4%) and 17 patients presented with transient ischemic attacks (10.7%). Hypertension was the most common modifiable risk factor seen in 78.0%, followed by diabetes in 40.3% and dyslipidemia in 31.5%. At presentation to clinic, only 26.0% patients with dyslipidemia and 64.5% patients with hypertension were on appropriate medications. CONCLUSION: A high prevalence of modifiable risk factors such as hypertension in stroke patients was observed and it presents an opportunity for conventional interventions in Pakistan. Systematized clinics for stroke and an algorithmic approach in primary care towards stroke may improve the implementation of evidence based secondary prevention strategies in developing countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".