Abstract WP147: Multimodal CT: Favorable Outcome Factors In Acute Middle Cerebral Artery Stroke With Large Artery Occlusion
Bibliographic record
Abstract
Background In acute stroke patients with large artery occlusion, we investigated the usefulness of multimodal CT as an initial predictor for a favorable outcome in addition to clinical findings. We studied which parameters of multimodal CT might be the most reliable predictors for favorable outcomes, and combination of these multimodal CT parameters to increase predictive validity, compared to a single parameter. Methods The parameters of multimodal CT, including non-enhanced CT (NECT), CT angiography, perfusion CT parameters, CT angiography source image (CTA-SI), and collateral flow, were analyzed in 66 consecutive patients with acute middle cerebral artery stroke with large artery occlusion. To evaluate the predictive validity of individual parameters for favorable outcome at 3 month follow-up (modified Rankin scale≤ 2), receiver operating characteristic (ROC) curves were generated, and optimum predictive cutoff- Alberta Stroke Program Early CT Score (ASPECTS) was calculated. ROC curves of the individual parameters and their combinations were analyzed by pairwise comparison. As an additional imaging predictor to clinical findings for favorable outcome, crude or adjusted odds ratios of individual parameters, predefined by the optimum predictive cutoff-ASPECTS, were assessed through univariate or multivariate analyses. Results Optimum predictive cutoffs for favorable outcome as follows: CTA-SI ASPECTS ≥ 7, cerebral blood volume (CBV) ASPECTS ≥ 6, NECT ASPECTS ≥ 10, good collateral flow, and cerebral blood flow ASPECTS ≥ 2. On the multivariate analyses, CTA-SI ASPECTS ≥ 7, CBV ASPECTS ≥ 6, and good collateral flow were associated with a favorable outcome. On the ROC analyses by pairwise comparison, the combination of those parameters had better predictive validity compared to a single parameter only: CBV (p = 0.039), CTA-SI (p = 0.038), and collateral flow (p < 0.001). Conclusion Among the various parameters of multimodal CT, CTA-SI ASPECTS ≥ 7, CBV ASPECTS ≥ 6, and good collateral flow might be the most reliable predictors for favorable outcomes in acute stroke patients with large artery occlusion. Moreover, considering these parameters simultaneously might improve the predictive validity of multimodal CT for functional outcome.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".