Stroke profile and outcome between urban and rural regions of Northwest India: Data from Ludhiana population-based stroke registry
Bibliographic record
Abstract
INTRODUCTION: The objective of this study is to compare the clinical profile, risk factors, type and outcome of stroke patients in urban and rural areas of Punjab, India. METHODS: The primary data source was from the Ludhiana urban population-based stroke registry. The data of first-ever stroke patients with age ≥18 years were collected using WHO stepwise approach from all hospitals, general practitioners, physiotherapy and scan centres between 26 March 2011 and 25 March 2013. RESULTS: A total of 4989 patients were included and out of 4989 patients, 3469 (69%) were from urban areas. Haemorrhagic stroke was seen more in rural as compared to urban regions (urban 1104 (32%) versus rural 552 (36%); p = 0.01). There were significant differences seen in stroke risk factors; hypertension (urban 1923 (84%) versus rural 926 (89%); p = 0.001) and hyperlipidaemia (urban 397 (18%) versus rural 234 (23%); p = 0.001) between two groups. In the multivariable analysis the rural patients were more likely to be younger (age < 40 years) (OR: 1.82; 95% CI: 1.24-2.68; p = 0.002), Sikhs (OR: 2.57; 95% CI: 1.26-5.22; p = 0.009), farmers (OR: 9.41; 95% CI: 5.36-16.50; p < 0.001), housewives (OR: 2.71; 95% CI: 1.45-5.06; p = 0.002), and consumed alcohol (OR: 1.57; 95% CI: 1.19-2.06; p = 0.001) as compared to urban patients. In addition, use of imaging was higher in rural patients (OR: 1.99; 95% CI: 1.06-3.74; p = 0.03) as compared to urban patients. DISCUSSION AND CONCLUSION: In this large cohort of patients, rural and urban differences were seen in risk factors and type of stroke. Stroke prevention strategies need to take into consideration these factors including regional sociocultural practices.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".