Abstract W P272: Healthcare Resource Availability, Acute Ischemic Stroke Outcomes and Quality of Care
Bibliographic record
Abstract
Background: Healthcare resources vary by geographic region, but the association between hospital-based resources and acute stroke outcomes has not been well-characterized. Methods: We used data from Get With The Guidelines-Stroke (GWTG-Stroke), a national, inpatient quality improvement initiative, and the Dartmouth Atlas of Healthcare (DAH) to examine the association between regional healthcare resource availability, stroke care, and outcomes. We categorized each hospital resource region (HRR) according to whether it was above or below the 2006 national median in availability of six resources: neurologists, radiologists, ER physicians, rehabilitation specialists, hospital-based RNs, and inpatient beds. Each HRR was then classified as High (>50th percentile in at least 5 resource categories), Medium (>50th percentile in 3 or 4 categories), or Low resource (>50th percentile in fewer than 3 categories). We used multivariable logistic regression to examine healthcare resource level and in-hospital outcomes. Results: Of 1,480,308 ischemic stroke patients admitted from 2006-2013, 28.8% were hospitalized in low resource HRRs, 44.4% in medium resource HRRs, and 26.9% in high resource HRRs. Demographic distributions were similar across resource levels. In unadjusted models, global p-values were non-significant and all standardized differences between resource levels were <10% for the defect free care composite measure and all but one individual quality measure (discharge statin therapy). Adjusted length of stay and in-hospital mortality were similar across all resource levels, including in a sensitivity analysis adjusting for NIHSS (recorded in 60.7%; data not shown). Conclusions: Among hospitals participating in GWTG-Stroke, differences in regional resource availability did not influence acute ischemic stroke quality of care or in-hospital outcomes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".