Outcome prediction value of critical area perfusion score for acute basilar artery occlusion
Bibliographic record
Abstract
PURPOSE: To investigate the performance of the Critical Area Perfusion Score (CAPS), based on computed tomography perfusion (CTP) time to maximum (Tmax) > 10s maps, to predict the outcome in acute basilar artery occlusion (BAO) in patients undergoing endovascular treatment (EVT). METHODS: We perform a retrospective analysis of a prospectively collected database of acute BAO treated with EVT in a comprehensive stroke center. The favorable outcome was defined as the 90-day modified Rankin Scale (mRS) ≤ 3. We performed the logistic regression analysis to find the independent predictors of the favorable outcome. Then, we used receiver operating characteristic analyses to assess the predictive value of the imaging parameters, including CAPS, Posterior Circulation Alberta Stroke Program Early CT Score (PC-ASPECTS), pons midbrain index (PMI), posterior circulation computed tomography angiography (PC-CTA) score, Basilar Artery on Computed Tomography Angiography (BATMAN) score, and CTP parameters. Finally, the Delong test was used to compare the area under the curve (AUC) of CAPS against the other imaging parameters. RESULTS: Of the 65 enrolled patients, the incidence of the favorable outcome was 44.6% (29/65). Low CAPS (per 1- point increased odds ratio [OR], 0.43; 95% confidence interval [CI], 0.22-0.86; P = 0.017) and admission National Institutes of Health Stroke Scale (NIHSS) (per 1- point increased OR, 0.80; 95% CI, 0.70-0.91; P = 0.001) were independently associated with favorable outcome. The AUC of CAPS was 0.83 (95% CI, 0.74-0.93; P < 0.001) with ≤ 3 cut-off value, 89.66% sensitivity, 77.22% specificity, and 80.00% accuracy, which was greater than the other imaging parameters (All P for Delong test < 0.05). CONCLUSIONS: CAPS was the most accurate imaging-based outcome predictor in acute BAO patients. Future large prospective multicenter studies are needed to verify these results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".