P.190 Choosing Endovascular Treatment or Thrombolysis in Patients with Pre-stroke Comorbidities: UNMASK EVT, a Worldwide Survey
Bibliographic record
Abstract
Background: Decisions to treat large-vessel occlusion with endovascular therapy(EVT) or intravenous alteplase depend on how physicians weigh benefits against risks when considering patients’ pre-stroke comorbidities. Methods: In an international survey, experts chose treatment approaches under current resources and under assumed ideal conditions for 10 of 22 randomly assigned case-scenarios. Five included comorbidities(metastatic/non-metastatic cancer, cardiac/respiratory/renal disease, non-disabling/mild cognitive impairment[MCI], physical dependence). We examined scenario/respondent characteristics associated with EVT/alteplase decisions using multivariable logistic regressions. Results: Among 607 physicians(38 countries), EVT was favoured in 1,097/1,379(79.6%) responses for comorbidity-related scenarios under current resources versus 1,510/1,657(91.1%,OR:0.38, 95%CI.0.31-0.47) for six “level-1A” scenarios (assuming ideal conditions:82.7% vs 95.1%,OR:0.25,0.19-0.33). However, this was reversed on including all other scenarios(e.g. under current resources:3,489/4,691[74.4%], OR:1.34,1.17-1.54). Responses favouring alteplase for comorbidity-related(e.g.75.0% under current resources) scenarios were comparable to level-1A scenarios(72.2%) and higher than all others(60.4%). No comorbidity-related factor independently diminished EVT-odds. MCI and dependence carried higher alteplase-odds; cancer and cardiac/respiratory/renal disease had lower odds. Relevant respondent characteristics included performing more EVT cases/year (higher EVT, lower alteplase-odds), practicing in East-Asia (higher EVT-odds), and in interventional neuroradiology(lower alteplase-odds vs neurology). Conclusions: Moderate-to-severe comorbidities did not consistently deter experts from EVT, suggesting equipoise about withholding EVT based on comorbidities. However, alteplase was often foregone when respondents chose EVT.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".