How Do Physicians Approach Intravenous Alteplase Treatment in Patients with Acute Ischemic Stroke Who Are Eligible for Intravenous Alteplase and Endovascular Therapy? Insights from UNMASK-EVT
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: With increasing use of endovascular therapy, physicians' attitudes toward intravenous alteplase in endovascular therapy-eligible patients may be changing. We explored current intravenous alteplase treatment practices of physicians in endovascular therapy- and alteplase-eligible patients with acute stroke using prespecified case scenarios and compared how their current local treatment practices differ compared with an assumed ideal environment. MATERIALS AND METHODS: In an international multidisciplinary survey, 607 physicians involved in acute stroke care were randomly assigned 10 of 22 case scenarios, among them 14 with guideline-based alteplase recommendations (9 with level 1A and 5 with level 2B recommendation) and were asked how they would treat the patient: A) under their current local resources, and B) under assumed ideal conditions. Answer options were the following: 1) anticoagulation/antiplatelet therapy, 2) endovascular therapy, 3) endovascular therapy plus intravenous alteplase, and 4) intravenous alteplase. Decision rates were calculated, and multivariable regression analysis was performed to determine variables associated with the decision to abandon intravenous alteplase. RESULTS: < .001). Under assumed ideal conditions, interventional neuroradiologists would refrain from intravenous alteplase most often (6.28%, OR = 2.40; 95% CI, 1.01-5.71). When physicians' current and ideal decisions differed, most would like to add endovascular therapy to intravenous alteplase in an ideal setting (196/3861 responses, 5.1%). CONCLUSIONS: In patients eligible for endovascular therapy and intravenous alteplase, we observed a slightly lower decision rate in favor of intravenous alteplase under assumed ideal conditions compared with the decision rate under current local resources.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".