Endovascular Therapy or Alteplase in Patients with Comorbidities: Insights from UNMASK EVT
Bibliographic record
Abstract
OBJECTIVE: Decisions to treat large-vessel occlusion with endovascular therapy (EVT) or intravenous alteplase depend on how physicians weigh benefits against risks when considering patients' comorbidities. We explored EVT/alteplase decision-making by stroke experts in the setting of comorbidity/disability. METHODS: In an international multi-disciplinary survey, experts chose treatment approaches under current resources and under assumed ideal conditions for 10 of 22 randomly assigned case scenarios. Five included comorbidities (cancer, cardiac/respiratory/renal disease, 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 chosen less often in comorbidity-related scenarios (79.6% under current resources, 82.7% assuming ideal conditions) versus six "level-1A" scenarios for which EVT/alteplase was clearly indicated by current guidelines (91.1% and 95.1%, respectively, odds ratio [OR] [current resources]: 0.38, 95% confidence interval 0.31-0.47). However, EVT was chosen more often in comorbidity-related scenarios compared to all other 17 scenarios (79.6% versus 74.4% under current resources, OR: 1.34, 1.17-1.54). Responses favoring alteplase for comorbidity-related scenarios (e.g. 75.0% under current resources) were comparable to level-1A scenarios (72.2%) and higher than all others (60.4%). No comorbidity independently diminished EVT odds when considering all scenarios. MCI and dependence carried higher alteplase odds; cancer and cardiac/respiratory/renal disease had lower odds. Being older/female carried lower EVT 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). CONCLUSION: 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. Differences in decision-making by patient age/sex merit further study.
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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 | 0.003 | 0.023 |
| 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 | 0.000 | 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".