The problem of strict image-based inclusion criteria for mechanical thrombectomy – an analysis of stroke patients with an initial low CBV-ASPECTS score
Bibliographic record
Abstract
Introduction Endovascular treatment for acute ischaemic stroke with large artery occlusion has become the standard of care. However, the question if a subgroup of patients, with a low cerebral blood volume Alberta Stroke Program Early CT score (CBV-ASPECTS) ≤ 7 should be excluded from endovascular treatment remains open. Therefore; we investigated the difference of outcome between patients who were treated by endovascular treatment vs patients who did not receive endovascular treatment. Methods We retrospectively analysed our stroke database for all patients who presented within six hours of onset with unfavourable imaging findings and who received endovascular treatment or best medical treatment alone. Unfavourable imaging was defined as a CBV-ASPECTS ≤ 7, which was an exclusion criterion for endovascular treatment at our institution before 2015. Results From 60 patients with an initial CBV-ASPECTS ≤ 7, 40 received best medical treatment and 20 were treated with endovascular treatment. Arterial hypertension and atrial fibrillation was more present in patients without endovascular treatment, the other baseline characteristics and percentage of patients treated with intravenous recombinant tissue plasminogen activator were not significantly different in both groups. At discharge, 40% of the interventional treated patients had a favourable outcome (eight of 20 (40%) vs six of 40 (15%; p = 0.031). The median values of the National Institute of Health Stroke Score and modified Rankin Scale at discharge were significantly lower in the treated cohort (6.5 (2.5–10.5) vs 16 (9.5–22.5); p = 0.006; 3 (0–5.5) vs 5 (4.5–5.5); p = 0.003). Conclusion Patients with a CBV-ASPECTS ≤ 7 are likely to benefit from therapy and therefore may not be excluded from endovascular treatment. Further randomised trials are warranted to validate the data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".