Healthcare Resource Availability, Quality of Care, and Acute Ischemic Stroke Outcomes
Bibliographic record
Abstract
BACKGROUND: Healthcare resources vary geographically, but associations between hospital-based resources and acute stroke quality and outcomes remain unclear. METHODS AND RESULTS: Using Get With The Guidelines-Stroke and Dartmouth Atlas of Health Care data, we examined associations between healthcare resource availability, stroke care, and outcomes. We categorized hospital referral regions with high-, medium-, or low-resource levels based on the 2006 national per-capita availability median of 6 relevant acute stroke care resources. Using multivariable logistic regression, we examined healthcare resource level and in-hospital quality and outcomes. Of 1 480 308 admitted ischemic stroke patients (2006-2013), 28.8% were hospitalized in low-, 44.4% in medium-, and 26.9% in high-resource hospital referral regions. Quality-of-care/timeliness metrics, adjusted length of stay, and in-hospital mortality were similar across all resource levels. CONCLUSIONS: Significant variation exists in regional availability of healthcare resources for acute ischemic stroke treatment, yet among Get With the Guidelines-Stroke hospitals, quality of care and in-hospital outcomes did not differ by regional resource availability.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".