Systematic evaluation of predictive models for futile recanalization before thrombectomy in patients with acute ischemic stroke
Bibliographic record
Abstract
Objective: To systematically review existing predictive models for futile recanalization after mechanical thrombectomy in patients with acute ischemic stroke, in order to provide a basis for treatment decision-making. Methods: Relevant studies on predictive models of futile recanalization after mechanical thrombectomy for acute ischemic stroke were searched in PubMed, Web of Science, Embase, The Cochrane Library, CNKI, Wanfang, and VIP databases from inception to May 5, 2024. Reference lists were also manually searched as supplements. Two researchers independently performed the literature search, screening, and data extraction, and conducted risk of bias and quality assessments. Because most included studies did not provide 95% confidence intervals or standard errors of AUC values, a formal quantitative meta-analysis of model performance was not feasible. Instead, we conducted a stratified descriptive synthesis of AUC values according to modeling approach (traditional regression vs. machine learning/deep learning). Results: Thirteen studies were included, encompassing 23 predictive models for futile recanalization. Variables used in the models mainly involved baseline clinical and imaging features. The most frequently included predictors were age, NIHSS score, baseline mRS score, and baseline Alberta Stroke Program Early CT Score (ASPECTS). The AUC of the models ranged from 0.650 to 0.981, with 11 models reporting AUC values ≥0.8, indicating high predictive performance. Conclusion: Predictive models for futile recanalization after mechanical thrombectomy in acute ischemic stroke are still under development. While many models exhibit good discrimination, they commonly face a high risk of bias. Future research should emphasize external validation and optimization of existing models to improve their performance, reduce bias, and promote clinical implementation. Systematic review registration: The systematic review was registered in PROSPERO under the ID CRD42022382797. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022382797.
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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 | 0.033 | 0.132 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".