Agreement Among Stroke Faculty and Fellows in Treating Ischemic Stroke Patients With Tissue-Type Plasminogen Activator and Thrombectomy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The aim of this study is to determine agreement among vascular neurology fellows and faculty in treating patients with acute ischemic stroke with intravenous tissue-type plasminogen activator and intra-arterial thrombectomy (IAT). METHODS: Patients were evaluated simultaneously by at least 2 vascular neurology. Agreement was determined using kappa (κ) and intraclass correlation coefficients. RESULTS: In 60 patients, agreement was substantial for tissue-type plasminogen activator (κ=0.75 [95% confidence interval, 0.57-0.92]) and IAT (κ=0.63 [95% confidence interval, 0.30-0.96]), with no difference between fellow-fellow versus fellow-faculty. Intraclass correlation coefficient for National Institutes of Health Stroke Scale was 0.94 (95% confidence interval, 0.90-0.97) and κ for Alberta Stroke Program Early CT Score was 0.53 (95% confidence interval, 0.20-0.78). Rapidly improving or mild deficits caused disagreement for both tissue-type plasminogen activator and IAT, whereas interpretation of computed tomographic perfusion led to disagreement for IAT. CONCLUSIONS: We found substantial agreement between vascular neurology fellows and faculty in treating with tissue-type plasminogen activator or IAT. Areas for improvement include recognition of stroke mimics, consensus on treating less severe strokes, and use/interpretation of imaging.
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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 | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".