Baseline ASPECT Scores predict extent of stroke: Interim CT Analysis of CASES
Bibliographic record
Abstract
21 Background CT scanning remains the fastest and easiest method of neuro-imaging in acute ischemic stroke. The ASPECT score has been shown to be useful in assessing acute CT scans and has demonstrated validity and reliability. Previous studies have not prospectively evaluated early CT ischemia using ASPECTS. Methods 115 CT scans from the CASES study have been reviewed to date. All baseline and 24–48h follow-up scans were examined by a panel of 3 reviewers. Rating was done by 1 neuroradiologist and 2 of 3 neurologists per session. Consensus was achieved by majority opinion (2 of 3) on each data point. Reviewers were aware of the symptom side but blind to other patient characteristics. Results 79% of CT scans showed some degree of early ischemic change (ASPECTS Discussion Early ischemic change is common on baseline CT scans. A minority of CT scans may be over-interpreted using ASPECTS. Baseline ASPECT score predicts the regions of infarction in follow-up.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".