ACCESS
Bibliographic record
Abstract
OBJECTIVES: Our primary objective was to determine the proportion of the population able to achieve acute cerebrovascular care in emergency stroke systems (ACCESS) in the United States. In addition, we examined how policy changes, including allowing ground ambulances to cross state lines and allowing air ambulances to transport patients from the prehospital setting to primary stroke centers (PSCs), would affect population access to stroke care. DESIGN: Data were obtained via the US Census Bureau, The Joint Commission, and the Atlas and Database of Air Medical Services. Driving distances, ambulance driving speeds, and prehospital times were estimated using validated models and adjusted for population density. Access was determined by summing the population that could reach a PSC within the specified time intervals. SETTING/ PARTICIPANTS: US population. MAIN OUTCOME MEASURES: Thirty-, 45-, and 60-minute access by ground and air ambulance to PSCs. RESULTS: Fewer than 1 in 4 Americans (22.3%) have access to a PSC within 30 minutes, less than half (43.2%) have access within 45 minutes, and just over half (55.4%) have access within 60 minutes. The use of air ambulances to deliver patients to PSCs would increase access from 22.3% to 26.0% for 30 minutes, 43.2% to 65.5% for 45 minutes, and from 55.4% to 79.3% for 60 minutes. The combination of prehospital regionalization and air ambulance transport of patients with acute stroke would reduce the 135.7 million Americans without 60-minute access to a PSC by half, to 62.9 million. CONCLUSIONS: About half of the US population has timely access to a PSC. The use of air ambulances to triage patients with ischemic stroke to a PSC would increase the percentage of the US population with prompt access to stroke care. These data have implications for the ongoing design of the US stroke system.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.482 | 0.228 |
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".