Abstract WP380: Hospital-Level Variability in Diagnostic Testing and Ischemic Stroke Subtype Documentation
Bibliographic record
Abstract
Objective: Documentation of ischemic stroke subtype has clinical and research implications. We aimed to assess hospital-level variability in subtype documentation and diagnostic testing patterns in the Get With The Guidelines (GWTG)-Stroke registry. Methods: We identified patients admitted with ischemic stroke to GWTG-Stroke participating hospitals between January 1, 2016 and September 30, 2017. Sites were instructed on use of the TOAST criteria for subtype documentation. We assessed hospital-level variability in TOAST subtype documentation and, among those with subtype documented, the performance of echocardiography, cerebrovascular imaging, and cardiac rhythm monitoring. Results: Among 607,563 patients with ischemic stroke from 1,906 sites, 348,715 (57.4%) had documented ischemic stroke subtype. Considerable hospital-level variability was observed in subtype documentation (Figure A). Patients with subtype documentation were more likely to be inter-facility transfers and treated at higher volume and academic centers, have complete medical history data, and have higher rates on achievement and quality measures. Carotid and intracranial vascular imaging (69.1% and 58.7%, respectively), echocardiography (74.3%), and cardiac rhythm monitoring (76.2%) were performed most frequently in cryptogenic stroke (CS) patients compared to other subtypes (Figure B; p<0.001 for each comparison). Among CS patients, short-term cardiac rhythm monitoring (65.7%) was most common with only 6.1% undergoing extended surface cardiac rhythm monitoring and 4.4% receiving extended implantable cardiac rhythm monitoring. Conclusions: In a large contemporary nationwide dataset of acute ischemic stroke hospitalizations, we observed that stroke subtype is documented in 57.4% of records, raising an important opportunity for quality improvement. Furthermore, diagnostic testing patterns suggest incomplete evaluation is common, even among patients with CS.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".