P.144 Patient-relevant deficit dictates EVT decision-making in low NIHSS patients with medium vessel occlusion stroke
Bibliographic record
Abstract
Background: There are no recommendations regarding endovascular treatment (EVT) for patients with acute ischemic stroke (AIS) due to primary medium vessel occlusion (MeVO). The aim of this study was to examine the willingness to perform EVT among stroke physicians in patients with mild, yet personally-disabling deficits due to MeVO. Methods: In an international survey consisting of 4 cases of primary MeVOs, participants were asked whether the presence of personally-disabling deficits would influence their decision-making for EVT despite the patients having low NIHSS scores. Decision rates were calculated based on physician characteristics. Clustered univariable logistic regression was performed. Results: 366 participants from 44 countries provided 2562 answers. 56.9% opted to perform EVT in scenarios in which the deficit was relevant to the patient’s profession versus 41.0% in which no information regarding patient profession was provided (RR1.39, p<0.001). The largest effect sizes were seen for female participants (RR1.68, 95%CI:1.35-2.09), participants >60 years (RR1.61, 95%CI:1.23-2.10), with more neurointervention experience (RR1.60, 95%CI:1.24-2.06), and who personally performed >100 EVTs per year (RR1.63, 95%CI:1.22-2.17). Conclusions: The presence of a patient-relevant deficit in low NIHSS AIS due to MeVO is an important factor for EVT decision-making. This may have relevance for the conduct and interpretation of low NIHSS EVT randomized trials.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".