Usefulness of Z-Score Mapping for Quantification of Extent of Hypoattenuation Regions of Hyperacute Stroke in Unenhanced Computed Tomography
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the usefulness of z-score mapping method on neuroradiologists' performance in quantification of the extent of hypoattenuation regions of hyperacute stroke on unenhanced computed tomographic (CT) images by using the Alberta Stroke Programme Early CT Score system. METHODS: Twenty-one patients with infarction (<3 hours) were retrospectively selected. Five neuroradiologists interpreted CT images first without and then with z-score maps by using the Alberta Stroke Programme Early CT Score system. Their performances in the quantification of the extent of hypoattenuation were compared. RESULTS: Average accuracies for the quantification without and with the z-score maps were 82.6% and 86.6%, respectively (P < 0.0001). The average area under the receiver operating characteristic curve for detection of focal hypoattenuation significantly increased from 0.883 to 0.925 (P = 0.01) by use of z-score maps. CONCLUSIONS: The use of z-score mapping method has the potential to help neuroradiologists quantify the extent of hypoattenuation regions of hyperacute stroke on unenhanced CT images.
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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 | 0.003 | 0.016 |
| 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 | 0.001 |
| 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".