Validation of a System to Predict Recanalization After Endovascular Treatment of Intracranial Aneurysms
Bibliographic record
Abstract
BACKGROUND: With increasing use of endovascular techniques in the treatment of ruptured and unruptured aneurysms, the issue of obliteration efficacy has become increasingly important. We have previously reported the Aneurysm Recanalization Stratification Scale, which uses accessible predictors including aneurysm-specific factors (size, rupture, and intraluminal thrombosis) and treatment-related features (treatment modality and immediate angiographic result) to predict retreatment risk after endovascular therapy. OBJECTIVE: To assess the external validity of the Aneurysm Recanalization Stratification Scale. METHODS: External validity was assessed in independent cohorts from 4 centers in the United States and Canada where endovascular and open neurovascular procedures are performed, and in a multicenter cohort of 1543 patients. Probability of retreatment stratified by risk score was derived for each center and the combined multicenter cohort. RESULTS: Despite moderate variability in retreatment rate among centers (29.5%, 9.9%, 9.6%, 26.3%, 19.7%, and 18.3%), the Aneurysm Recanalization Stratification Scale demonstrated good predictive value with C-statistics of 0.799, 0.943, 0.780, 0.695, 0.755, and 0.719 for each center and the combined cohort, respectively. Probability of retreatment stratified by risk score for the combined cohort is as follows: -2, 4.9%; -1, 5.7%; 0, 5.8%; 1, 13.1%; 2, 19.2%; 3, 34.9%; 4, 32.7%; 5, 73.2%; 6, 89.5%; and 7, 100.0%. CONCLUSION: Surgical decision-making and patient-centered informed consent require comprehensive and accessible information on treatment efficacy. The Aneurysm Recanalization Stratification Scale is a valid prognostic index. This is the first comprehensive model that has been developed to quantitatively predict retreatment risk following endovascular 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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".