Regional Variation in 30-Day Ischemic Stroke Outcomes for Medicare Beneficiaries Treated in Get With The Guidelines–Stroke Hospitals
Bibliographic record
Abstract
Background— We explored regional variation in 30-day ischemic stroke mortality and readmission rates and the extent to which regional differences in patients, hospitals, healthcare resources, and a quality of care composite care measure explain the observed variation. Methods and Results— This ecological analysis aggregated patient and hospital characteristics from the Get With The Guidelines–Stroke registry (2007–2011), healthcare resource data from the Dartmouth Atlas of Health Care (2006), and Medicare fee-for-service data on 30-day mortality and readmissions (2007–2011) to the hospital referral region (HRR) level. We used linear regression to estimate adjusted HRR-level 30-day outcomes, to identify HRR-level characteristics associated with 30-day outcomes, and to describe which characteristics explained variation in 30-day outcomes. The mean adjusted HRR-level 30-day mortality and readmission rates were 10.3% (SD=1.1%) and 13.1% (SD=1.1%), respectively; a modest, negative correlation ( r =−0.17; P =0.003) was found between one another. Demographics explained more variation in readmissions than mortality (25% versus 6%), but after accounting for demographics, comorbidities accounted for more variation in mortality compared with readmission rates (17% versus 7%). The combination of hospital characteristics and healthcare resources explained 11% and 16% of the variance in mortality and readmission rates, beyond patient characteristics. Most of the regional variation in mortality (65%) and readmission (50%) rates remained unexplained. Conclusions— Thirty-day mortality and readmission rates vary substantially across HRRs and exhibit an inverse relationship. While regional variation in 30-day outcomes were explained by patient and hospital factors differently, much of the regional variation in both outcomes remains unexplained.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".