Ischemic Stroke Management in India: Insights from the Indian Registry of Stroke Care Quality
Bibliographic record
Abstract
INTRODUCTION: The quality of stroke care in India is unknown. This multicentric study from the Indian Registry of Stroke Care Quality (RES-Q) provides prospectively entered stroke data from 46 stroke centers across the country. We analyzed demographics, therapeutic interventions, quality metrics, and functional outcomes of ischemic stroke from India. METHODS: It was an analysis of prospectively entered data into the RES-Q from 2022 to 2024. The inclusion criteria were patients ≥18 years of age with an acute ischemic stroke or transient ischemic attack based on the AHA/ASA 2019 criteria within 2 weeks of stroke onset. All demographic, clinical, and radiological details were entered into a predesigned proforma. All quality and performance measures in the hospital were collected. RESULTS: Approximately 80% of all strokes (n = 7,337) during the study period were ischemic. The median age was 61 years (interquartile range [IQR] 52-71), with 67% being male. The median National Institute of Health Stroke Scale at admission was 7 (IQR 4-12). Brain parenchymal imaging was done for all patients, but vascular imaging was performed for 60% of patients only. The thrombolysis rate was 32% with a median door-to-needle time of 37 min. Endovascular thrombectomy was performed in 6.5% of cases. Swallowing assessment was done in 72% of patients within 24 h of admission and 90% received physiotherapy in the stroke unit. CONCLUSION: This landmark dataset marks the first comprehensive nationwide effort to assess stroke care quality in India. Routine quality monitoring through platforms like RES-Q can help standardize care, reduce disparities, and enable hospitals to benchmark performance and implement targeted improvements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".