Impact of Insurance Status on Outcomes and Use of Rehabilitation Services in Acute Ischemic Stroke: Findings From Get With The Guidelines‐Stroke
Bibliographic record
Abstract
BACKGROUND: Insurance status affects access to care, which may affect health outcomes. The objective was to determine whether patients without insurance or with government-sponsored insurance had worse quality of care or in-hospital outcomes in acute ischemic stroke. METHODS AND RESULTS: Multivariable logistic regressions with generalized estimating equations stratified by age under or at least 65 years were adjusted for patient demographics and comorbidities, presenting factors, and hospital characteristics to determine differences in in-hospital mortality and postdischarge destination. We included 589 320 ischemic stroke patients treated at 1604 US hospitals participating in the Get With The Guidelines-Stroke program between 2012 and 2015. Uninsured patients with hypertension, high cholesterol, or diabetes mellitus were less likely to be taking appropriate control medications prior to stroke, to use an ambulance to arrive to the ED, or to arrive early after symptom onset. Even after adjustment, the uninsured were more likely than the privately insured to die in the hospital (<65 years, OR 1.33 [95% CI 1.22-1.45]; ≥65 years OR 1.54 [95% CI 1.34-1.75]), and among survivors, were less likely to go to inpatient rehab (<65 OR 0.63 [95% CI 0.6-0.67]; ≥65 OR 0.56 [95% CI 0.5-0.63]). In contrast, patients with Medicare and Medicaid were more likely to be discharged to a Skilled Nursing Facility (<65 years OR 2.08 [CI 1.96-2.2]; OR 2.01 [95% CI 1.91-2.13]; ≥65 years OR 1.1 [95% CI 1.07-1.13]; OR 1.41 [95% CI 1.35-1.46]). CONCLUSIONS: Preventative care prior to ischemic stroke, time to presentation for acute treatment, access to rehabilitation, and in-hospital mortality differ by patient insurance status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".