Abstract WP205: Development and Validation of a Prediction Model for Outcome in Mechanical Thrombectomy for Large-Vessel Occlusion Anterior Circulation Stroke With Low ASPECTS
Bibliographic record
Abstract
Introduction: Recent randomized control trials suggested that mechanical thrombectomy (MT) was associated with good functional outcomes after acute ischemic stroke (AIS) due to large vessel occlusion (LVO) in patients presenting with low Alberta Stroke Program Early CT Score (ASPECTS) (defined as ASPECTS 2-5). The aim of this study is to develop and validate a stroke prediction tool for outcome in MT for AIS patients with low ASPECTS using data from an ongoing international multicenter registry, the Stroke Thrombectomy and Aneurysm Registry (STAR). Methods: 236 AIS patients with low ASPECTS caused by LVO who undertook MT between January 2010 and December 2022 were retrospectively investigated. Univariate and multivariate logistic regression results were used to screen model predictors and construct nomograms of 90-day modified Rankin Scale scores (mRS) 0-3. The performance of the model was detected by using receiver operating characteristic analysis. The bootstrap resampling method was considered internal validation of the model. Results: Age (< 70 years), premorbid status (mRS 0), National Institutes of Health Stroke Scale (NIHSS) (< 20), and recanalization status after the MT (modified Thrombolysis in Cerebral Ischemia [mTICU] ≥2b) were related to 90-day mRS 0-3. Predictive score was calculated by adding 1 point for age (< 70 years), premorbid status (mRS 0), and NIHSS < 20 and 3 points for a mTICI ≥2b (ranging 0-6). 90-day mRS 0-3 was observed in 0% of patients with a score of 0 or 1, 6.3% with a score of 2, 17.7% with a score of 3, 22.2% with a score of 4, 45.7% with a score of 5, and 73.7% with a score of 6. The score showed relatively high performance in predicting 90-day mRS0-3 (area under the curve: 0.79 [95% CI 0.73-0.79] and 0.78 [95% CI 0.78-0.78] for derivation and validation cohorts, respectively). Conclusions: This study indicates the STAR score can be calculated with baseline and periprocedural characteristics to predict the 90-day outcome after MT in AIS patients with low ASPECTS.
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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 | 0.006 | 0.013 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".