Abstract P133: Geospatial Mapping of Prehospital Delay in Acute Ischemic Stroke and Association With Social Vulnerability
Bibliographic record
Abstract
Introduction: Prehospital delay, defined as the delay between symptom discovery and hospital arrival, remains a major barrier to timely acute stroke treatments. Delay is worse in socially vulnerable populations. A geospatial map of prehospital delay may identify high-risk areas and highlight the role of community social vulnerability in delay. We hypothesized that a community’s social vulnerability would be associated with delay. Methods: We analyzed national Get With The Guidelines ischemic stroke data between 2015 and 2017. We calculated the median arrival time (symptom discovery-to-door times) for each Zip Code Tabulation Area (ZCTA), and created geospatial map using ArcGIS. The primary exposure variable was the Center for Disease Control’s Social Vulnerability Index (SVI), and its 4 subcomponents. The SVI is a composite metric of community vulnerability using U.S. Census data (0, least vulnerable to 1, most vulnerable). To account for clustering within ZCTAs, we performed a multilevel linear regression of community-level SVI and patient-level prehospital delay. Results: During the study period, 149,774 patients had an ischemic stroke in 16,949 ZCTAs. Across patients, the median time of arrival was 140 mins, IQR was 60-459 mins, and range was 1-1439 mins. Arrival by 2h occurred in 46% of patients. Multilevel regression showed a strong positive association between the SVI and prehospital delay, evident in the maps (Figure). For every 10% increase in the SVI, the arrival time increased by 38 minutes [CI, 30 - 47] (p<0.001). Considering the 4 SVI subcomponents, delay was most strongly associated with socioeconomic status, household composition, and housing/transportation, but not minority status/language. Conclusion: Using geospatial mapping of prehospital delay across the United States, we show that community SVI is strongly associated with delayed ischemic stroke arrival. These maps help identify communities to target for stroke preparedness campaigns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".