Abstract 3023: The Association of Insurance Status, Quality of Care, and In-Hospital Outcomes among Patients Hospitalized with Intracerebral Hemorrhage: Findings from GWTG-Stroke
Bibliographic record
Abstract
Introduction Intracerebral hemorrhage (ICH) can be a devastating condition, requiring intensive intervention. Yet, few studies have examined whether patient insurance status is associated with ICH care or acute outcomes. Methods Using data from 1,711 sites participating in GWTG-Stroke database from April 2003 to April 2011, we identified 156,848 non-transferred subjects with ICH who had known discharge status. Insurance status was categorized as private, Medicaid, Medicare or none. We explored associations between lack of insurance (using private insurance status as the reference group) and in-hospital outcomes (mortality, ambulatory status, & length of stay) and quality of care measures (DVT prophylaxis, smoking cessation, dysphagia screening, stroke education, imaging times, & rehabilitation). We utilized multiple individual (including demographics and medical history) and hospital (including size, geographic region and academic teaching status)lcharacteristics as covariates. Results Subjects without insurance (n=10647) were younger (54.4 v. 71 years), more likely men (60.6 v. 50.8%), more likely black (33.2 v. 17.4%) or Hispanic (15.8 v. 7.9%), from the South (50.6 v. 38.9%), and had fewer vascular risk factors with the exception of smoking when compared with the overall subject population. Further, subjects without insurance were more likely to experience in-hospital mortality (25.9 v. 23.9%; adjusted OR 1.29) and longer length of stay (11.4 v. 7.8 days), but were more likely to receive all quality measures of care, be discharged home (52.1 v. 36.1%), and ambulate independently (47.5 v. 38.5%) at discharge compared with subjects with private insurance (n=40033). Conclusions Among GWTG-Stroke participating hospitals, ICH patients without insurance were more likely to die while in the hospital but experienced higher quality measures of care and were more likely to ambulate independently at discharge should they survive.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".