Abstract WP381: Cryptogenic Stroke: Contemporary Characteristics, Treatments, and Outcomes in the United States
Bibliographic record
Abstract
Objective: Nationwide data on patients with cryptogenic stroke (CS) are lacking. We evaluated patient and hospital characteristics, in-hospital treatments, and discharge outcomes among CS patients compared to other subtypes in the Get With The Guidelines (GWTG)-Stroke registry. Methods: We identified patients admitted to GWTG-Stroke participating hospitals between January 1, 2016 and September 30, 2017 with 1) ischemic stroke and 2) documented stroke etiology (cardioembolic [CE], large artery atherosclerosis [LAA], small vessel occlusion [SVO], other determined etiology [OTH], or CS). Using multivariable logistic regression, we compared discharge outcomes by subtype adjusted for patient and hospital characteristics. Results: Among 348,715 patients from 1,725 hospitals with documented stroke subtype, there were 69,857 (20.0%) patients with CS. Compared to CE subtype, patients with CS were younger, less likely to arrive by ambulance, less often white, more privately insured, and milder by NIHSS score. In multivariable analysis (Table), patients with CS had lower mortality than CE, LAA, and OTH subtypes but higher mortality than SVO. Patients with CS were more likely to be discharged home than all subtypes and be independent at discharge than patients with LAA or OTH subtypes. Conclusions: In a large nationwide registry, CS accounted for 20% of ischemic stroke subtypes. Patients with CS had lower stroke severity than CE stroke subtype and had intermediate outcomes at discharge being better than CE and LAA subtypes, but worse than SVO subtype.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".