Abstract WP63: Infarct Location Predicts Outcome Following Distal Middle Cerebral Artery Occlusion
Bibliographic record
Abstract
Background: Although it is generally thought that patients with distal middle cerebral artery (MCA) occlusion have a favorable outcome, it has previously been demonstrated that a substantial minority will have a poor outcome by 90 days. We sought to determine whether the infarct location information encoded in the Alberta Stroke Program Early CT Score (ASPECTS) allows for defining distinct ASPECTS regions that are associated with a poor outcome to improve outcome prediction in in these patients. Methods: We retrospectively analyzed patients with isolated acute distal MCA occlusion admitted to a single academic center between January 2010 to August 2012. Infarct regions were defined according to ASPECTS system on the initial head CT. Discriminant function analysis was used to define specific ASPECTS regions that are predictive of the 90 day functional outcome. In addition, logistic regression was used to model the relationship between each individual ASPECT region with poor outcome; for evaluation and comparison, odds ratios, c-statistics, and Akaike information criterion values were estimated for each region. Results: 90 patients with isolated distal MCA were included in the final analysis. ASPECTS score ≤ 6 predicted poor outcome in this cohort (sensitivity=0.591, specificity=0.838, p<0.001). Using multiple statistic approaches we found that infarction in ASPECTS regions M3 and M6 were strongly associated with poor functional status (mRS 3-6) by 90 days. Conclusion: ASPECTS regions M3 and M6 are key predictors of functional outcome following isolated distal MCA infarction. These findings will be helpful in stratifying outcomes if validated in future studies.
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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 | 0.000 | 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) | 0.002 | 0.001 |
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".