The Effectiveness of Support and Training for Setting up Organized Stroke Care Pathways in Improving the Clinical Outcome Following a Stroke (OSCAIL)
Bibliographic record
Abstract
Abstract TextBackground: Among the proven treatment strategies for stroke unit care has the maximum popular impact. It is unclear whether stroke unit care is beneficial in remote hospitals in low and middle income countries (LMIC). Hence we developed this study u201cThe Effectiveness of Support and Training for Setting up Organised Stroke Care Pathways in Improving the Clinical Outcome Following a Stroke -OSCAILu201d in India. Objective: 1. To determine the effectiveness of support and training provided to stroke teams within hospital to implement stroke care pathways.2. To increase the proportion of patients receiving 3 or more key stroke care interventions (KSI) by ~10%.Methods: This study is being undertaken in four LMICs with two centres each. Two centres in India will be participating in this study. After obtaining informed consent 280 patients will be recruited. The stroke unit staff will be provided:- support to set up specialist multidisciplinary stoke teams- training- checklist and standing order templatesAll data will be managed centrally at PHRI, McMaster University and Hamilton Health Sciences, Canada.Results: We will compare the provision of stroke care and outcome of stroke between two phases of the study, between study sites in each country as well as between each country to demonstrate the feasibility of this intervention at national and international levels. Data collection will start in two centres in India soon.Conclusions: If proven beneficial such OSC can be implemented across hospitals in LMICs to improve delivery of stroke care.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".