Perfusion in Early Stroke Visualization and Assessment R.I. Aviv
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Qualitative CT perfusion (CTP) assessment by using the Alberta Stroke Program Early CT Score (ASPECTS) allows rapid calculation of infarct extent for middle cerebral artery infarcts. Published thresholds exist for noncontrast CT (NCCT) ASPECTS, which may distinguish outcome/complication risk, but early ischemic signs are difficult to detect. We hypothesized that different ASPECTS thresholds exist for CTP parameters versus NCCT and that these may be superior at predicting clinical and radiologic outcome in the acute setting. MATERIALS AND METHODS: Thirty-six baseline acute stroke NCCT and CTP studies within 3 hours of symptoms were blindly reviewed by 3 neuroradiologists, and ASPECTS were assigned. Treatment response was defined as major neurologic improvement when a8-point National Institutes of Health Stroke Scale improvement at 24 hours occurred. Follow-up NCCT ASPECTS and 90-day modified Rankin score (mRS) were radiologic and clinical reference standards. Receiver operating characteristic curves derived optimal thresholds for outcome. RESULTS: Cerebral blood volume and NCCT ASPECTS had similar radiologic correlations (0.6 and 0.5, respectively) and best predicted infarct size in the absence of major neurologic improvement. A NCCT ASPECT threshold of 7 and a cerebral blood volume threshold of 8 discriminated patients with poor
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".