Futile recanalization after mechanical thrombectomy in patients with acute ischemic stroke and large ischemic core
Bibliographic record
Abstract
OBJECTIVE: Multiple randomized controlled trials have demonstrated the efficacy of mechanical thrombectomy (MT) for acute ischemic stroke with a large ischemic core caused by large-vessel occlusion. Despite successful recanalization, more than half of the patients do not achieve a favorable prognosis, a phenomenon referred to as futile recanalization (FR). We aimed to identify the risk factors for, and incidence of, FR in patients with large ischemic cores. METHODS: Eighty-four patients with a large ischemic core who underwent MT between January 2015 and December 2024 at three hospitals were retrospectively reviewed. Patients were divided into two groups-FR and no-FR-according to functional independence at 90 days (modified Rankin Scale (mRS) score ≥4). Factors influencing FR were identified using multivariate logistic regression and receiver operating characteristic curve analyses. RESULTS: Eighty-four patients fulfilled the inclusion criteria, and FR was observed in 57 patients (67.9%). Multivariable regression analysis revealed that older age (odds ratio [OR], 1.09; 95% confidence interval [CI], 1.01-1.18; P=0.011), concomitant diabetes (OR, 11.2; 95% CI, 1.13-111.1; P=0.012), diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score (OR, 0.32; 95% CI, 0.11-0.79; P=0.012), and an increased number of passes (OR, 1.91; 95% CI, 1.00-4.16; P=0.046) were independently associated with FR after MT. CONCLUSIONS: Older age, concomitant diabetes, diffusion-weighted imaging-Alberta Stroke Program Early Computed Tomography Score, and an increased number of passes are independently associated with FR after MT in patients with a large ischemic core.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".