Abstract TP56: Endovascular Treatment Decisions in Acute Ischemic Stroke Patients With Low Baseline Aspects: Insights From an International Multidisciplinary Survey
Bibliographic record
Abstract
Background and Purpose: Current AHA/ASA guidelines for the early management of patients with acute ischemic stroke restrict level 1A recommendations for endovascular therapy (EVT) to patients with baseline ASPECTS score >5. However, a recent meta-analysis from the HERMES group showed a treatment benefit in patients with ASPECTS ≤5. We aimed to explore how physicians across different specialties and countries approach endovascular treatment decision-making in acute ischemic stroke patients with low baseline ASPECTS. 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, 3 of which involved patients with baseline ASPECTS < 6 (A: 40-year old with ASPECTS 4, B: 33-year old with ASPECTS 2 C: 72-year old with ASPECTS 3), otherwise fulfilling all EVT-eligibility criteria. Participants were 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. without any external (monetary, policy-related or infrastructural) restraints. Overall and scenario-specific decision rates were calculated. Clustered multivariable logistic regression analysis was used to determine variables associated with EVT decision in patients with low baseline ASPECTS. Results: 827/6070 responses were available for the low ASPECTS scenarios. Current and ideal treatment EVT decision rates were 57.1% and 57.6% respectively. Current and ideal decision rates were 69.9% and 60.4% for scenario A, 60.0% and 61.5% for scenario B, 41.3% and 40.2% for scenario C respectively. Annual center EVT volume (OR 1.004,p=.004), annual operator EVT volume (OR 1.009, p=.018) and time since symptom onset (OR 4.543,p<.001) were significantly associated with EVT decision-making under current local resources, while annual operator EVT volume (OR 1.007,p<.029) and time since symptom onset (OR 5.687,p<.001) were associated with decision-making under assumed ideal conditions. Conclusion: A majority of physicians decided to proceed with EVT despite low baseline ASPECTS. Operators and centers doing more EVT per year were more likely to offer EVT to patients with low ASPECTS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.001 |
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".