Age-specific ASPECTS atlas of Chinese subjects across different age groups for assessing acute ischemic stroke
Bibliographic record
Abstract
The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a valuable and easy-to-use method for assessing acute ischemic stroke. It aids in identifying suitable candidates for thrombolytic therapies and evaluating treatment effectiveness. However, ASPECTS evaluation primarily relies on visual observation in current clinical practice, lacking a common standardized space. Additionally, different doctors may have varying clinical experiences, leading to a poor inter-reader agreement and potential errors in the final ASPECTS scoring. To address these issues and fill in the absence of a publicly available ASPECTS atlas, this work constructs age-specific Chinese ASPECTS atlases based on non-contrast computed tomography images of 281 healthy subjects across different age groups. Images of different age groups are warped into respective common averaged spaces, where the average intensity atlases are computed. More importantly, 10 ASPECTS regions can be obtained during this process. We develop an automated ASPECTS region mapping pipeline and collect an independent dataset to validate our atlas. The results prove that the age-specific ASPECTS atlas is of great promise in clinical availability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".