P.145 Perceived Limits of Endovascular Treatment for Secondary Medium Vessel Occlusion Stroke
Bibliographic record
Abstract
Background: Thrombus embolization during endovascular treatment (EVT) occurs in up to 9% of cases, making secondary medium-vessel occlusions (MeVOs) of particular interest to neurointerventionalists. We sought to gain insight into the current EVT approaches for secondary MeVO stroke in an international case-based survey as there are currently no clear recommendations for EVT in these patients. Methods: Participants were presented with three secondary MeVO cases, each consisting of three case-vignettes with changes in patient neurological status (improvement, no change, unable to assess). Clustered multivariable logistic regression analyses were used to assess factors influencing the decision to treat. Results: 366 physicians from 44 countries took part. The majority (54.1%) were in favor of EVT. Participants were more likely to treat occlusions in the anterior M2/3 (74.3%; risk ratio [RR]2.62, 95%CI:2.27-3.03) or A3 (59.7%; RR2.11, 95%CI:1.83-2.42) segment, compared to the M3/4 segment (28.3%;reference). Physicians were less likely to pursue EVT in patients with neurological improvement (49.9% versus 57.0%; RR0.88, 95%CI:0.83-0.92). Interventionalists and more experienced physicians were more likely to treat secondary MeVOs. Conclusions: Physician’s willingness to treat secondary MeVOs endovascularly is limited and varies per occlusion location and change in neurological status. More evidence on the safety and efficacy of EVT for secondary MeVO stroke is needed.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".