Hypodensity of >1/3 Middle Cerebral Artery Territory Versus Alberta Stroke Programme Early CT Score (ASPECTS)
Bibliographic record
Abstract
Background— The one third middle cerebral artery territory (⅓ MCA) method and the Alberta Stroke Program Early CT Score (ASPECTS) were used to detect significant early ischemic changes (EIC) on CT brain of acute stroke patients. We sought to compare the reliability of the 2 methods in routine clinical practice. Methods— Eighty consecutive patients admitted to a community hospital in Hong Kong with suspected acute ischemic stroke and a CT brain scan performed within 6 hours of symptom onset were included. Five blinded observers (1 neurologist, 2 general radiologists, and 2 neuroradiologists) independently evaluated the scans, using the ATLANTIS/CT Summit criteria for >⅓ MCA involvement, and ASPECTS ≤7. Kappa statistics were used to determine interobserver agreement. Results— Significant EIC were present in 11.4% of the scans with the ⅓ MCA method, and 19.4% with ASPECTS. For >⅓ MCA involvement, all observers agreed in 57 cases (71%), with moderate interobserver agreement (κ=0.49). For ASPECTS ≤7, all observers agreed in 34 cases (42%), with fair interobserver agreement (κ=0.34). After prevalence and bias adjustments, substantial (prevalence-adjusted bias-adjusted κ [PABAK]=0.74) and moderate (PABAK=0.44) agreements were found for the ⅓ MCA method and ASPECTS respectively. Conclusions— The ⅓ MCA method was more reliable in detecting significant EIC on CT brain within 6 hours of stroke onset in routine clinical practice, whereas ASPECTS was able to detect significant EIC in a higher proportion of these early scans.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".