Value of computed tomography angiographic collateral status in prediction of malignant middle cerebral artery infarction
Bibliographic record
Abstract
Background: Cerebral collateral circulation is necessary to maintain cerebral blood flow and penumbra when arterial insufficiency occurred. Only a few studies about collateral status on development of malignant middle cerebral artery infarction (mMCAi) have been documented.
 Objective: To determine whether collateral status evaluated by single phase computed tomographic angiography (CTA) help prediction of mMCAi in patients with large arterial occlusion whom not received endovascular treatment.
 Material and Methods: We retrospectively reviewed patients with acute ischemic stroke in anterior circulation in our institute during January 2015 to December 2015. We analyzed clinical data, baseline National Institutes of Health Stroke Scale (NIHSS), Alberta Stroke Program Early CT Score (ASPECTS) on baseline nonenhanced computed tomography of the brain (NECT brain), and CTA collateral status. Malignant MCA infarction was defined according to clinical criteria.
 Results: Thirty-five patients were included. Mean age was 68.8±15.56 years. Mean baseline NIHSS and baseline ASPECTS were 17(±5) and 6(±3), respectively. All patients received intravenous thrombolysis. CTA collateral status and baseline NECT ASPECTS significantly correlated with development of mMCAi (P-value = 0.007 and 0.001). Only baseline NECT ASPECTS was an independent predictive factor for mMCAi (OR 0.63, 95%CI 0.46-0.86, P-value =0.004). Patients with baseline NECT ASPECTS ? 7 were more likely develop mMCAi (OR 14.29 95%CI 1.57-129.94, P-value 0.018).
 Conclusion: In acute stroke patients with proximal MCA or ICA occlusion received intravenous thrombolysis alone, baseline NECT ASPECTS and CTA collateral status were significantly correlate with development of mMCAi. However, only baseline ASPECTS ? 7 was an independent predictor for mMCAi.
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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 | 0.001 |
| 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) | 0.000 | 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".