Hospital‐Level Variability in Reporting of Ischemic Stroke Subtypes and Supporting Diagnostic Evaluation in GWTG‐Stroke Registry
Bibliographic record
Abstract
BACKGROUND: Secondary prevention of ischemic stroke (IS) requires adequate diagnostic evaluation to identify the likely etiologic subtype. We describe hospital-level variability in diagnostic testing and IS subtyping in a large nationwide registry. METHODS AND RESULTS: We used the GWTG-Stroke (Get With The Guidelines-Stroke) registry to identify patients hospitalized with a diagnosis of acute IS at 1906 hospitals between January 1, 2016, and September 30, 2017. We compared the documentation rates and presence of risk factors, diagnostic testing, achievement/quality measures, and outcomes between patients with and without reported IS subtype. Recording of diagnostic evaluation was optional in all IS subtypes except cryptogenic, where it was required. Of 607 563 patients with IS, etiologic IS subtype was documented in 57.4% and missing in 42.6%. Both the rate of missing stroke pathogenesis and the proportion of cryptogenic strokes were highly variable across hospitals. Patients missing stroke pathogenesis less frequently had documentation of risk factors, evidence-based interventions, or discharge to home. The reported rates of major diagnostic testing, including echocardiography, carotid and intracranial vascular imaging, and short-term cardiac monitoring were <50% in patients with documented IS pathogenesis, although these variables were missing in >40% of patients. Long-term cardiac rhythm monitoring was rarely reported, even in cryptogenic stroke. CONCLUSIONS: Reporting of IS etiologic subtype and supporting diagnostic testing was low overall, with high rates of missing optional data. Improvement in the capture of these data elements is needed to identify opportunities for quality improvement in the diagnostic evaluation and secondary prevention of stroke.
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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 | 0.077 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".