Quality of Care and Ischemic Stroke Risk After Hospitalization for Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND: Patients with transient ischemic attack (TIA) are at increased risk for ischemic stroke. We derived a prediction rule for 1-year ischemic stroke risk post-TIA, examining estimated risk, receipt of inpatient quality of care measures for TIA, and the presence or absence of stroke at 1 year post discharge. METHODS AND RESULTS: We linked 67 892 TIA Get With The Guidelines-Stroke patients >65 years (2003-2008) to Medicare inpatient claims to obtain longitudinal outcomes. Using Cox proportional hazards modeling in a split sample, we identified baseline demographics and clinical characteristics associated with ischemic stroke admission during the year post-TIA, and developed a Get With The Guidelines Ischemic Stroke after TIA Risk Score; performance was examined in the validation sample. Quality of care was estimated by a global defect-free care measure, and individual performance measures within estimated risk score quintiles. The overall hospital admission rate for ischemic stroke during the year post-TIA was 5.7%. Patients with ischemic stroke were more likely to be older, black, and have higher rates of smoking, previous stroke, diabetes mellitus, previous myocardial infarction, heart failure, and atrial fibrillation. The Risk Score showed moderate discriminative performance (c-statistic=0.606); highest quintile patients were less likely to receive statins, smoking cessation counseling, and defect-free care. Although not associated with 1-year ischemic stroke, DCF was associated with a significantly lower risk of all-cause mortality. CONCLUSIONS: TIA patients with high estimated ischemic stroke risk are less likely to receive defect-free care than low-risk patients. Standardized risk assessment and delivery of optimal inpatient care are needed to reduce this risk-treatment mismatch.
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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 | 0.001 | 0.001 |
| 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".