Evaluating the Role of Post-Thrombectomy MRI in Predicting Functional Recovery in Basilar Artery Occlusion
Bibliographic record
Abstract
ABSTRACT Background: Stroke due to basilar artery occlusion (BAO) carries poor outcomes despite advancements in endovascular therapy (EVT). Predictors of recovery remain underexplored. This study evaluates clinical and imaging predictors of outcomes following EVT, including a Critical Area Diffusion Score (CADS), assessing infarct location and extent in critical brainstem regions on post-EVT MRI. Methods: This retrospective study analyzed 48 BAO patients treated with EVT at a provincial stroke center (2015–2021). Patients were categorized by outcomes (favorable: modified Rankin Scale [mRS] 0–3; unfavorable: mRS 4–6). Clinical, demographic and imaging data – age, baseline National Institutes of Health Stroke Scale (NIHSS), reperfusion success (thrombolysis in cerebral infarction [TICI] 2b–3) and post-EVT CADS from diffusion-weighted MRI – were assessed using univariate and multivariate logistic regression. Results: Patients with favorable outcomes were younger (median 64.0 vs. 73.0 years, p = 0.031), had lower NIHSS scores at presentation (median 8 vs. 16, p = 0.018) and achieved higher successful reperfusion rates (81.0% vs. 48.1%, p = 0.020). CADS ≤ 3 was linked to better outcomes (median mRS 3 vs. 5, p = 0.026) and higher odds of recovery in univariate analysis (OR = 10.89, p = 0.038). However, in multivariate analysis, CADS was not an independent predictor, with successful reperfusion (OR = 18.8, p = 0.044) as the strongest factor. Conclusion: Age, NIHSS scores and successful reperfusion predict recovery in BAO patients undergoing EVT. Although CADS ≤ 3 is linked to favorable outcomes, it is not independently predictive. CADS holds promise as a prognostic tool, but its utility should be considered alongside clinical markers to enhance outcome prediction in BAO.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".