Abstract WMP43: Disparities in Large Vessel Occlusion Stroke Imaging and Endovascular Treatment Metrics, and Procedural Exclusions in the Global Registry of Stroke Care Quality (Res-Q)
Bibliographic record
Abstract
Introduction: Global access to mechanical thrombectomy for large vessel occlusion (LVO) stroke is low and disparate between regions. Comparative global data on time metrics for imaging and endovascular treatment (EVT) performance, as well as the reasons for excluding EVT, are lacking. Methods: This is a cross-sectional study using quality metrics from the Res-Q registry on patients presenting directly to the hospital with acute ischemic stroke from January 1, 2022, to December 31, 2022, in countries with 200 or more cases. We used descriptive statistical methods to study the time metrics of interest including door to imaging and door to puncture. Additionally, reasons for not performing thrombectomy in patients with LVO were collected (Table 1). Results: Among the 153,181 patients from 912 hospitals across 67 countries enrolled in the Res-Q registry, 125,390 had an acute ischemic stroke. After excluding secondary transfers (n=26,648), patients with missing or erroneous data (n=42,232), and countries with less than 200 cases (n=1,626), data from a total of 54,884 patients from 631 hospitals across 30 countries were analyzed. CT angiography (CTA) was performed in 24,215 (44.1%) patients and EVT was done in 3,649 (6.6%) patients. For patients who had simultaneous CT+CTA, the median door-to-CTA time was 27.5 min (IQR 22.5-33.9) and the median door-to-puncture time was 98.5 (IQR 79.8-129.3) minutes. The most prevalent specific reason for excluding EVT in patients with LVO on CTA/MRA was the presentation in the late time window (42.0%). Only 11 (36.7%) countries achieved a median door-to-puncture time within 90 minutes (Table 1). Conclusions: The performance of acute imaging for LVO detection and time metrics for EVT appear to be disparate between countries and warrant further study. Additional data from global stroke care quality registries are needed to set benchmarks, compare regional stroke systems of care, and identify gaps to mobilize resources appropriately.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".