Unequal Efficacy of Different Infarct Location in Predicting Futile Recanalization of Patients With Acute Ischemic Stroke
Bibliographic record
Abstract
Objectives Endovascular thrombectomy (EVT) is a standard treatment for acute ischemic stroke (AIS) caused by large vessel occlusion, while futile recanalization is the main factor influencing the prognosis. The present study aimed to investigate the efficacy of different infarct sites in predicting futile recanalization of patients with AIS. Methods Data were obtained from two multicenter, prospective, randomized, and controlled trials, which were concurrently conducted in China. Cases achieving a successful recanalization and with complete data of preoperative Alberta Stroke Program Early CT score (ASPECTS) and 90-day follow-up were included. The ASPECTS subregions were used to mark different infarct locations in the two cerebral hemispheres. First, the distribution of each ASPECTS subregion in the left and right hemispheres and the whole brain was analyzed, respectively. Then, the regions associated with futile recanalization were initially assessed by a univariate model. Afterward, a multivariate logistic regression model was used to identify the efficacy of different infarct sites in predicting futile recanalization. Results A total of 336 patients were included in this study with a median age of 65 years (IQR: 51–74), of whom 210 (62.50%) patients were male, and 189 (56.25%) met the definition of futile recanalization. The correlation between each ASPECTS subregion and poor outcome was different when it was restricted to a specific cerebral hemisphere. Moreover, in the left hemisphere, the internal capsule region (OR: 1.42, 95%CI: 1.13–1.95, P = 0.03) and the M3 region (OR: 2.26, 95%CI: 1.36–3.52, P = 0.001), and in the right hemisphere, M6 region (OR: 2.24, 95%CI: 1.32–3.36, P = 0.001) showed significantly higher efficacy in predicting futile recanalization. Conclusion The efficacy of different infarct locations in predicting futile recanalization is different. Different preoperative patterns of the high-efficiency regions in the infarction core or penumbra can guide the thrombectomy decision-making.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".