Incomplete or failed thrombectomy in acute stroke patients with Alberta Stroke Program Early Computed Tomography Score 0–5 – how harmful is trying?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: It is currently unknown whether mechanical thrombectomy (MT) for ischaemic stroke patients with low initial Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is clinically beneficial or even harmful. The purpose of this study was to investigate whether failed or incomplete MT in acute large vessel occlusion stroke with an initial ASPECTS ≤ 5 is associated with worse clinical outcome compared to patients not undergoing MT. METHODS: This observational cohort study included a consecutive sample of patients with anterior circulation stroke and initial ASPECTS ≤ 5 admitted between March 2015 and August 2019. Failed recanalization was defined as Thrombolysis in Cerebral Infarction (TICI) score 0-2a, and incomplete recanalization as TICI 2b. Clinical outcome was assessed using the modified Rankin Scale (mRS) at 90 days defining very poor clinical outcome as mRS > 4. RESULTS: One hundred and seventy patients were included. Ninety-nine patients underwent MT and 71 patients received best medical treatment only. Clinical outcome after failed or incomplete MT (TICI 0-2b) was significantly better compared to patients with medical treatment only (median mRS 5, interquartile range 4-6 vs 5-6, P = 0.03). In multivariable logistic regression analysis, failed or incomplete MT (TICI 0-2b) showed a significantly reduced likelihood for very poor outcome (odds ratio 0.39, 95% confidence interval 0.19-0.83, P = 0.01). Failed MT (TICI 0-2a) was not associated with a worse outcome compared to best medical treatment. CONCLUSIONS: Patients with failed or incomplete recanalization results (TICI 0-2b) showed a reduced likelihood for very poor outcome compared with those who did not receive MT. Evidence from randomized trials is needed to confirm that even failed or incomplete MT is not harmful in these patients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".