Abstract TP53: How Do Physicians Approach Intravenous Alteplase Treatment in Acute Ischemic Stroke Patients? Insights From Unmask Evt, an International Multidisciplinary Study
Bibliographic record
Abstract
Background and Purpose: With increasing use of endovascular therapy (EVT), physician attitudes towards intravenous alteplase in EVT-eligible patients may be changing. We explored current intravenous alteplase treatment practices of physicians and compared how their current treatment practice differs to an assumed ideal environment. 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 recommendation for intravenous alteplase treatment, and asked how they would treat the patient: A) under their current local resources and B) under assumed ideal conditions, i.e. with no external restraints. Answer options were 1) anticoagulation/antiplatelet therapy, 2) EVT, 3) EVT plus intravenous alteplase and 4) intravenous alteplase . Decision rates were calculated and clustered multivariable regression analysis was performed to determine adjusted measures of effect size. Results: Physicians favored intravenous alteplase in 82.0% (85.0% in level 1A scenarios and 76.5% in level 2B scenarios) under current local resources and in 79.3% (82.4% in level 1A scenarios and 73.7% in level 2B scenarios) under assumed ideal conditions (difference between current and ideal rates: p<0.001 respectively). This discrepancy was driven by physicians who favored EVT alone rather than EVT in combination with intravenous alteplase . Interventional neuroradiologists favored dropping intravenous alteplase most often (6.28%), and this specialty was associated with greater odds of dropping intravenous alteplase (OR 1.97, p=.041). Conclusion: Participants of this survey currently favoured treating slightly more patients with intravenous alteplase than they would like to treat in an ideal environment.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".