Novel Approaches in Evaluating and Predicting the Potential Benefit of Middle Cerebral Artery Angioplasty & Stenting
Bibliographic record
Abstract
OBJECTIVE: Our aim was to investigate a novel approach to perform preoperative evaluation patients who underwent middle cerebral artery (MCA) percutaneous transluminal angioplasty and stenting (PTAS). METHOD: Sixty-five patients with symptomatic MCA stenosis of at least >70% who underwent MCA PTAS were enrolled. The multimodal stroke assessment using CT (MOSAIC) score was used to evaluate the preoperative condition. The Alberta Stroke Program Early Computed Tomography Scoring (ASPECTS) was used to assess the time-to-peak (TTP) parameter of Computer tomography perfusion (CTP). The factors potentially improving TTP following stenting were investigated. The prognostic value of the MOSAIC scores to predict TTP improvement was analyzed and compared. RESULTS: The MOSAIC score was a reliable prognostic tool for the degree of improvement of TTP (odds ratio 1.89 [1.08-2.07], P < .01) in patients with PTAS. The MOSAIC score had a higher prognostic accuracy than the degree of CBF deficit, the degree of stenosis, and the amount of tissue infarction. During 1-year follow-up, the stroke and death rate of was 8.1%, the in-stent restenosis rate was 6.5%, and good final outcome (modified Rankin Scale ≤ 2) was observed in 76.9%. CONCLUSIONS: The MOSAIC score can be reliably used in selecting patients with MCA stenosis for PTAS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".