Abstract WMP53: Pre-hospital Arrival Times in 25 countries across regions: A Cross-Country Analysis from Registry of Stroke Care Quality (RES-Q)
Bibliographic record
Abstract
Background: Treatment options and outcomes in stroke also depends on pre-hospital delays. The goal of this study is to describe onset-to-door times (ODT) across many countries and also investigate how the mode of arrival affects ODT. Methods: This is an analysis of the data from the Registry of Stroke Care Quality (RES-Q), years 2022&2023. RES-Q is used across the world for audit of clinical care. Data were stratified by the mode of arrival (EMS vs. non-EMS). Median ODT were analyzed, and 95% confidence intervals (CI) were calculated for each country and arrival mode. Results: Of 334,184 patients from 1,130 hospitals in 70 countries, 155,532 patients from 25 countries were diagnosed with acute ischemic stroke after excluding secondary transfers (n=32,349), cases from countries with fewer than 1,000 cases (n=128,660), and those with missing data or typing errors (n=17,643). The median ODT was 193 mins (95% CI: 164-223) for patients arriving by EMS and 309 mins (95% CI: 274-360) for those arriving by non-EMS. The percentage of EMS arrival by region was 34% (Africa), 30% (Asia), 39% (Latin America), and 87% (Europe). The percentage of EMS arrivals is shown in Figure 1. Compared to the patients who reached by non-EMS mode, patients who reached by EMS mode were more likely to receive intravenous thrombolysis (16% vs 44%,). The ODTs by mode of arrival and country are detailed in Figure 2. Conclusions: Transport via EMS was associated with a reduced arrival time nearly by 2 hours and tripled the chance of receiving thrombolysis as compared to non-EMS transportation. The percentage of patients arriving by EMS was higher in European countries as compared to Africa, Asia and Latin America and this is reflected by shorter ODT in many EU countries. Improvements in EMS infrastructure could improve stroke outcomes 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".