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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".