Abstract WP31: Endovascular Treatment Decision Making in Octo- and Nonagenarians: Insights From UNMASK EVT, an International Multidisciplinary Study
Bibliographic record
Abstract
Background and Purpose: Efficacy and cost effectiveness of EVT in elderly stroke patients (≥80 years) is controversial, since they were underrepresented in randomized trials. We sought to explore how physicians approach endovascular therapy decision-making in octo- and nonagenarians under their current local resources and investigated how they would change their treatment decision under assumed ideal conditions, i.e. without external (monetary or infrastructural) limitations. Methods: In an international multidisciplinary survey, 607 physicians involved in acute stroke care were randomly assigned 10 out of a pool of 22 case-scenarios with different evidence levels for EVT, 4 of which involved octogenarians and 2 nonagenarians, and asked how they would treat the patient in the given scenario A) under their current local resources and B) under assumed ideal conditions, i.e. with no external restraints. Decision rates were calculated and clustered multivariable regression analysis performed to determine adjusted measures of effect size. Results: Most physicians across the world decided to treat patients > 80 years with EVT (figure). In octogenarians, physicians decided in favor of EVT in 76.7% (all of which were level 2B evidence scenarios) under current local resources and in 80.2% under assumed ideal conditions. In nonagenarians, 74.0% decided in favor of EVT under current local resources (level 1A scenarios: 87.7%, level 2B scenarios: 60.3%) and 79.2% would offer EVT under assumed ideal conditions (level 1A scenarios: 91.3%, level 2B scenarios: 67.2%). Age was not a significant predictor for treatment decision under current local resources (OR 1.00, p=.317) and under assumed ideal conditions (OR 1.00, p=.708). Conclusion: The vast majority of physicians participating in this survey decided to offer EVT to acute ischemic stroke patients above 80 years.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".