Profile of Modifiable and Non-Modifiable Risk Factors in Stroke in a Rural Based Tertiary Care Hospital – A Case Control Study
Bibliographic record
Abstract
BACKGROUND: Stroke, a major public health problem in India and worldwide, is associated with many risk factors. The modification of risk factors, an important public health strategy, has been shown to reduce the risk of stroke. Hence the present study was carried out to document the risk factor profile of stroke. METHODS: It was a case-control study. Patients with stroke admitted in a tertiary care centre in central India and age and sex matched controls were included. Detail history and clinical examination was done in all cases and controls. The risk factors studied were education, socioeconomic status (according to Kuppuswamy's classification), level of physical activity, alcohol intake, and smoking, tobacco chewing, family history of stroke and history of systemic hypertension, transient ischemic attack or ischemic heart disease. Anthropometric (weight, height, body mass index and waist circumference) measurements were done in all patients. Electrocardiogram was done in cases as well as controls and abnormalities noted. STATISTICAL ANALYSIS: The data was analyzed using Epi info version 3.4.1 software. Chi-square test was used as test of significance and p value less than 0.05 was considered as significant. RESULTS: On comparing the cases with controls, sedentary life-style (p=0.02), history of transient ischemic attack (p=0.002), coronary artery disease (p=0.014), family history of stroke (p=0.001), systemic hypertension (p<0.001) and ECG abnormalities (p=0.04) were significant risk factors whereas low socio-economic status (p=0.40), smoking (p=0.12), tobacco chewing (p=0.35), alcohol consumption (p=0.22), obesity [both central and generalized as assessed by waist circumference (p=0.33) and BMI respectively (p=0.43)] and Diabetes mellitus (p=0.07) were not found to be statistically significant risk factors. The most significant risk factor was systemic hypertension (OR= 15.92, 95% CI, 1.78-6.85) followed by coronary artery disease (OR=3.86, 95% CI, 1.13-14.50), abnormal ECG (OR=2.49, 95% CI, 0.97-6.96) and sedentary life-style (OR=2.41, 95% CI, 1.07-5.49). CONCLUSIONS: In the present hospital based case control study in patients with stroke, sedentary life-style, history of transient ischemic attack, family history of stroke, coronary artery disease, systemic hypertension and abnormal ECG were significant risk factors. This could be helpful in early identification of subjects at risk for stroke and formulating public health strategy, if proven by larger population based studies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".