Predictive value of ischemia location on multimodal CT in thrombectomy-treated patients
Bibliographic record
Abstract
OBJECTIVE: Alberta Stroke Program Early CT Score (ASPECTS) applied to CT-perfusion (CTP) and CT-angiography-source-images (CTA-SI) may improve outcome prediction in large vessel occlusion (LVO) stroke if compared to non-contrast CT (NCCT) alone. Besides, ischemia location may have enhanced capabilities, compared to ischemia volume alone, in predicting stroke outcomes. We aim to evaluate the association between ischemia location as measured by ASPECTS regions in NCCT, CTP maps and CTA-SI and 3 months outcome in patients with LVO treated with mechanical thrombectomy (MT). MATERIAL AND METHODS: Consecutive patients with anterior circulation stroke treated with MT were recorded in a prospectively maintained database at a single center. Modified Rankin scale (mRS) at 3 months >2 was considered a poor outcome. Association of patients' characteristics, NCCT, CTP, and CTA-SI parameters with outcome was evaluated using single-variable analysis and binary logistic regression multivariate analysis for each imaging technique. RESULTS: 177 patients were included. 115 (65%) patients reached a favorable outcome. The involvement of lenticular, caudate, M1, or M2 in all imaging techniques, insula in NCCT and CTA-SI and M5 in CBV maps and CTA-SI was related to functional outcome in bivariate analysis. However, in the multivariate analysis, none ischemia location was independently related to outcome, no matter the imaging technique studied. This finding remained unchanged when restricted to patients with good recanalization and when analyzing subpopulations according to hemisphere involvement or territories association. CONCLUSIONS: Our study suggests ischemia location shouldn't be used solely for decision-making in LVO stroke patients. Its predictive value may be taken in consideration together with other clinical and radiological variables.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".