Essential Workflow and Performance Measures for Optimizing Acute Ischemic Stroke Treatment in India
Bibliographic record
Abstract
troke is a leading cause of death in India, which has one-fifth of the world's population.The approximate age-adjusted prevalence of stroke in India is 84 to 262/100 000 in rural and 334 to 424/100 000 in urban areas, and the age-adjusted incidence of stroke is 135 to 152/100 000 person-years.1,2 The recent data from the GBD study (Global Burden of Disease) also suggest a high burden of cardiovascular disease in India due to a major epidemiological transition leading to a surge in noncommunicable diseases over the past 2 decades.3 See related articles, p 1928, p 1932, p 1941, p 1951, p 1961 and p 1978 Stroke is incorporated in the National Program for Prevention and Control of Diabetes, Cardiovascular Disease and Stroke.4 The objectives of the program include behavior changes for lifestyle modification, screening, and prevention of noncommunicable diseases, capacity building, optimizing treatment at all levels of health care and surveillance systems for disease burden and monitoring.5 Stroke Registry program was started in 2012 to collect data about stroke patients and as of 2016, across India 62 institutions have registered with it.This venture was initiated by the Indian Council of Medical Research along with the National Center for disease informatics and research.6 An Indo-US Collaborative Stroke Registry was started with 5 major teaching institutes from India and one major institute from the United States to develop a registry with high-quality data.7,8 However, there are multiple barriers in the implementation of optimized care among patients with stroke at the level of the following:• patient (late arrival or low awareness of stroke symptoms, denial of stroke, financial incapacity, sociocultural practices or beliefs inhibiting access or seeking optimal stroke care) • hospital or health system level (personnel with expertise in managing stroke, management will and support, lack of adequate medical facilities or equipment, well-defined protocols and standard operating procedures as also policies, supporting policies, organizational context, or norms, which support the implementation of evidence-based care) • stroke specialists and professionals (stroke teams, competence, skill, awareness, confidence, and experience in dealing with clinical situations, ability to take timely clinical decisions and confidence to do so, motivations, attitudes, and willingness to provide evidence-based stroke care) • national health policies (political will, allocation of resources, reimbursement of costs to different health care sectors, to support stroke patients' access to optimal care and the regulatory frameworks or policies to support stroke care).9,10 The current article deals with the existing stroke care pathways, its challenges and possible future initiatives to optimize workflow in India.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".