Atlas-Based Topographical Scoring for Magnetic Resonance Imaging of Acute Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Alberta Stroke Program Early CT Score (ASPECTS), a 10-point scale, is a clinical tool for assessment of early ischemic changes after stroke based on the location and extent of a visible stroke lesion. It has been extended for use with MR diffusion-weighted imaging. The purpose of this work was to automate a MR topographical score (MR-TS) using a digital atlas to develop an objective tool for large-scale analyses and possibly reduce interrater variability and slice orientation differences. METHODS: We assessed 30 patients with acute ischemic stroke with a diffusion lesion who provided informed consent. Patients were imaged by CT and MRI within 24 hours of symptom onset. An MR-TS digital atlas was generated using the ASPECTS scoring sheet and anatomic MR data sets. Automated MR topographical scores (auto-MR-TS) were obtained based on the overlap of lesions on apparent diffusion coefficient maps with MR-TS atlas regions. Auto-MR-TS scores were then compared with scores derived manually (man-MR-TS) and with conventional CT ASPECTS scores. RESULTS: Of the 30 patients, 29 were assessed with auto-MR-TS. Auto-MR-TS was significantly lower than CT ASPECTS (P<0.001), but with a median difference of only 1 point. There was no significant difference between the auto-MR-TS and the man-MR-TS with a median difference of 0 points; 86% of patient scores differed by
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".