Analysis of the baseline multiphase computed tomographic angiography findings to predict clinical outcomes in patients with middle cerebral artery M1 occlusion treated with mechanical thrombectomy
Bibliographic record
Abstract
PURPOSE: We aimed to evaluate the predictive ability of baseline multiphase computed tomographic angiography (mCTA) findings and the time from symptom onset to imaging in predicting functional outcomes in patients with middle cerebral artery (MCA) M1 occlusion treated with mechanical thrombectomy (MT). METHODS: A total of 70 patients were evaluated retrospectively. The time between the onset of symptoms and imaging, thrombus density, estimated thrombus length, the Alberta Stroke Program Early CT Score (ASPECTS) on non-contrast CT, collateral circulation (CC), actual thrombus length, and clot burden score were assessed on mCTA images. Patients with a 90-day modified Rankin scale score of 0-2 were categorized as having good outcomes, whereas the others were categorized as having poor outcomes. The mCTA findings of patients with good and poor outcomes were compared, and binary logistic regression analysis was performed to identify independent predictors that could affect clinical outcomes. RESULTS: = 0.001) to predict good outcomes. CONCLUSION: Higher thrombus density and actual thrombus length shorter than 18.7 mm were associated with good clinical outcomes. However, no significant correlation was found between clinical outcomes and the ASPECTS, CC degree, or clot burden scores. CLINICAL SIGNIFICANCE: Thrombus length and density are associated with the clinical outcome of patients with MCA M1 occlusion treated with MT who have distal collateral filling sufficient to depict thrombus margins in mCTA.
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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 | 0.000 |
| 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 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".