Abstract WMP38: Access to Acute Stroke Care: Global Perspective From Registry of Stroke Care Quality (RES-Q)
Bibliographic record
Abstract
Introduction: Disparities in access to acute stroke care represents a major global challenge. Addressing such challenges requires a comprehensive understanding of the variations in stroke care quality between nations. Methods: We used data from the global Registry of Stroke Care Quality (RES-Q). RES-Q is web-based quality monitoring platform for the management of stroke care. Key quality metrics on patients with acute ischemic stroke admitted in 2022 were calculated: patients percentage arriving by Emergency Medical Services (EMS); patients percentage admitted to the Stroke Unit/Intensive Care Unit (ICU); Door-to-Imaging Time (DIT), Door-to-Needle Time (DNT) for intravenous thrombolysis. Results: Of 153 181 patients across 912 hospitals in 67 countries enrolled in the register, 125 390 had acute ischemic stroke. After excluding secondary transfers (n=26 648), missing or erroneous data (n=45 526), and countries with <200 cases (n=1 410), a total of 51,806 patients from 696 hospitals and in 30 countries were analyzed. The percentage of patients arriving at healthcare facilities by EMS varied from 3% to 98% (median 51%; IQR 26-87) and admitted to the Stroke Units/ICUs from 25% to 99% (median 78%; IQR 47-89). The median DIT was 32; IQR 21-39, with range 10-60 minutes. The median DNT was 44; IQR 31-52, with range 20-71 minutes. Median DNT of patients admitted directly to the CT scanner was 24 minutes (IQR 15-38), and, if admitted to the emergency department, DNT was 39 minutes (IQR 25-55). Data by quartiles (best is blue, worst is red) from each country are presented in a detailed Table below. Conclusion: Our study underscores the critical role of the global registry in enabling cross-national comparative analyses of acute stroke care quality. The findings highlight disparities and variations in critical aspects of acute stroke care, paving the way for targeted interventions, policy reforms, and enhanced collaboration among stakeholders to improve stroke care globally.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".