Predictors of futile recanalization after endovascular treatment in acute ischemic stroke: a multi-center study
Bibliographic record
Abstract
Background and objectives Endovascular thrombectomy (EVT) improves long-term outcomes and decreases mortality in ischemic stroke patients. However, a significant proportion of patients do not benefit from EVT recanalization, a phenomenon known as futile recanalization or reperfusion without functional independence (RFI). In this study, we aim to identify the major stroke risk factors and patient characteristics associated with RFI. Methods This is a retrospective cohort study of 297 consecutive patients with ischemic stroke who received EVT at three academic stroke centers in China from March 2019 to March 2022. Patient age, sex, modified Rankin Scale (mRS), National Institute of Health Stroke Scale (NIHSS), Alberta stroke program early CT score (ASPECTS), time to treatment, risk factors and comorbidities associated with cerebrovascular diseases were collected, and potential associations with futile recanalization were assessed. RFI was successful reperfusion defined as modified thrombolysis in cerebral infarction (mTICI) ≥ 2b without functional independence at 90 days (mRS ≥ 3). Results Of the 297 initial patients assessed, 231 were included in the final analyses after the application of the inclusion and exclusion criteria. Patients were divided by those who had RFI (n = 124) versus no RFI (n = 107). Older age (OR 1.041, 95% CI 1.004 to 1.073; p = 0.010), chronic kidney disease (OR 4.399, 0.904–21.412; p = 0.067), and higher 24-h NIHSS (OR 1.284, 1.201–1.373; p < 0.001) were independent predictors of RFI. Conversely, an mTICI score of 3 was associated with a reduced likelihood of RFI (OR 0.402, 0.178–0.909; p = 0.029). Conclusion In conclusion, increased age, higher 24-h NIHSS and lack of an mTICI score of 3 were independently associated with RFI and have potential prognostic values in predicting patients that are less likely to respond to EVT recanalization therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".