Patient-Relevant Deficits Dictate Endovascular Thrombectomy Decision-Making in Patients with Low NIHSS Scores with Medium-Vessel Occlusion Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There is a paucity of evidence regarding the safety of endovascular treatment for patients with acute ischemic stroke due to primary medium-vessel occlusion. The aim of this study was to examine the willingness among stroke physicians to perform endovascular treatment in patients with mild-yet-disabling deficits due to medium-vessel occlusion. MATERIALS AND METHODS: In an international cross-sectional survey consisting of 7 primary medium-vessel occlusion case scenarios, participants were asked whether the presence of personally disabling deficits would influence their decision-making for endovascular treatment despite the patients having low NIHSS scores (<6). Decision rates were calculated on the basis of physician characteristics. Univariable logistic regression clustered by respondent and scenario identity was performed. RESULTS: < .001). The largest effect sizes were seen for female participants (risk ratio = 1.68; 95% CI, 1.35-2.09), participants older than 60 years of age (risk ratio = 1.61; 95% CI, 1.23-2.10), those with more experience in neurointervention (risk ratio = 1.60; 95% CI, 1.24-2.06), and those who personally performed >100 endovascular treatments per year (risk ratio = 1.63; 95% CI, 1.22-2.17). CONCLUSIONS: The presence of a patient-relevant deficit in low-NIHSS acute ischemic stroke due to medium-vessel occlusion is an important factor for endovascular treatment decision-making. This may have relevance for the conduct and interpretation of low-NIHSS endovascular treatment in 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".