Persistent challenges in endovascular treatment decision-making for acute ischaemic stroke
Bibliographic record
Abstract
PURPOSE OF REVIEW: Although endovascular treatment (EVT) is the gold standard for treating acute stroke patients with large vessel occlusion (LVO), multiple challenges in decision-making for specific conditions persist. Recent evidence on a selection of patient subgroups will be discussed in this narrative review. RECENT FINDINGS: Two randomized controlled trials (RCTs) have been published in EVT of basilar artery occlusion (BAO). Large single arm studies showed promising results in Patients with low Alberta stroke program early CT score (ASPECTS) and more distal vessel occlusions. Recent data confirm patients with low National Institutes of Health Stroke Scale (NIHSS) despite LVO to represent a heterogeneous and challenging patient group. SUMMARY: The current evidence does not justify withholding EVT from BAO patients as none of the RCTs showed any signal of superiority of BMT alone vs. EVT. Patients with low ASPECTS, more distal vessel occlusions and patients with low NIHSS scores should be included into RCTs if possible. Without participation in a RCT, patients should be selected for EVT based on age, severity and type of neurological impairment, time since symptom onset, location of the ischaemic lesion and perhaps also results of advanced imaging.
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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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".