Hospital distance, socioeconomic status, and timely treatment of ischemic stroke
Bibliographic record
Abstract
<h3>Objective</h3> To determine whether lower socioeconomic status (SES) and longer home to hospital driving time are associated with reductions in tissue plasminogen activator (tPA) administration and timeliness of the treatment. <h3>Methods</h3> We conducted a retrospective observational study using data from the Get With The Guidelines–Stroke Registry (GWTG-Stroke) between January 2015 and March 2017. The study included 118,683 ischemic stroke patients age ≥18 who were transported by emergency medical services to one of 1,489 US hospitals. We defined each patient9s SES based on zip code median household income. We calculated the driving time between each patient9s home zip code and the hospital where he or she was treated using the Google Maps Directions Application Programing Interface. The primary outcomes were tPA administration and onset-to-arrival time (OTA). Outcomes were analyzed using hierarchical multivariable logistic regression models. <h3>Results</h3> SES was not associated with OTA (<i>p</i> = 0.31) or tPA administration (<i>p</i> = 0.47), but was associated with the secondary outcomes of onset-to-treatment time (OTT) (<i>p</i> = 0.0160) and in-hospital mortality (<i>p</i> = 0.0037), with higher SES associated with shorter OTT and lower in-hospital mortality. Driving time was associated with tPA administration (<i>p</i> < 0.001) and OTA (<i>p</i> < 0.0001), with lower odds of tPA (0.83, 0.79–0.88) and longer OTA (1.30, 1.24–1.35) in patients with the longest vs shortest driving time quartiles. Lower SES quintiles were associated with slightly longer driving time quartiles (<i>p</i> = 0.0029), but there was no interaction between the SES and driving time for either OTA (<i>p</i> = 0.1145) or tPA (<i>p</i> = 0.6103). <h3>Conclusions</h3> Longer driving times were associated with lower odds of tPA administration and longer OTA; however, SES did not modify these associations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".