Decision-Making for Endovascular Thrombectomy in Patients With Large Vessel Occlusions and Mild Neurological Deficit: A Consensus Statement
Bibliographic record
Abstract
Acute ischemic stroke patients with mild deficits (National Institutes of Health Stroke Scale [NIHSS] of 0-5) but confirmed large vessel occlusions (LVO) present a clinical challenge for endovascular thrombectomy (EVT) decisions due to limited evidence and the absence of clear guidelines. A Delphi consensus was conducted at the 2024 5T (Teamwork, Training, Technology, Technique, Transport) Think Tank conference with 40 international stroke experts. Following a systematic literature review, three iterative Delphi rounds were employed to explore EVT decision-making in strokes due to LVO with low NIHSS. Data were collected through surveys and in-person discussions, focusing on disability evaluation, imaging markers, procedural risk, and outcome scales. Consensus was achieved on key factors influencing EVT decisions. Experts emphasized the importance of symptom-specific disability (e.g., aphasia, vision loss) over NIHSS scores alone. Early neurological deterioration (END) was perceived as main concern in this patient population. Imaging markers such as proximal occlusion, poor collaterals, and large penumbra were expected to be predictors of END. The anticipated technical difficulty and patient-specific factors, such as independence and quality of life, also guided decisions. The Potential of rtPA for Ischemic Strokes With Mild Symptoms (PRISMS) trial definition of disabling deficits and the 9-level modified Rankin Scale were favored as outcome measures for future studies. EVT decisions for acute ischemic strokes with mild deficit but proven LVO require nuanced, individualized approaches beyond NIHSS thresholds. Disability assessment, imaging-based risk evaluation, and patient-centered discussions are critical for optimizing outcomes, emphasizing the need for further research and standardized guidelines.
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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 | 0.153 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| 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".