A Simple and Pragmatic Equation for Rapid Outcome Prediction in Endovascular Thrombectomy With Limited Information
Bibliographic record
Abstract
BACKGROUND: To rapidly predict outcomes of candidate for endovascular thrombectomy in time-sensitive situations with limited clinical information, we propose a simple but balanced approach, integrating National Institutes of Health Stroke Scale (NIHSS) and Alberta Stroke Program Early CT Score (ASPECTS). METHODS: This study utilized data from 2 independent registries to investigate the associations of NIHSS scores and ASPECTS with clinical outcomes of patients with stroke with large vessel occlusion in the anterior circulation who underwent endovascular thrombectomy and to evaluate the accuracy of a novel clinical-imaging equation in predicting these outcomes. The primary outcome was functional independence. RESULTS: A total of 2128 patients were included. Of these, 1052 (49.4%) achieved functional independence. ASPECTS, NIHSS scores, and age were identified as key predictors across all basic parameters. Clinical-imaging equations (with and without age adjustment, defined as ASPECTS-0.5×NIHSS-age×0.2 and ASPECTS-NIHSS×0.5) were developed. These equations exhibited superior discriminative ability (C statistic, 0.71 [95% CI, 0.68-0.74], 0.68 [95% CI, 0.65-0.70]) compared with traditional methods in the validation cohort. The prediction probability of functional independence by quartiles of clinical-imaging equation with age adjustment (≤-15.5, -15.5 to -12, -15.5 to -12, >-9.5) was 32% (95% CI, 17%-47%), 51% (95% CI, 43%-59%), 63% (95% CI, 58%-69%), and 77% (95% CI, 65%-90%) in the validation cohort. CONCLUSIONS: By integrating ASPECTS and NIHSS scores, our clinical-imaging equations improved rapid prediction of endovascular thrombectomy outcomes in time-sensitive situations such as patient transfers from primary stroke centers or in mobile stroke units, where clinical information is limited.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".