Per-region interobserver agreement of Alberta Stroke Program Early CT Scores (ASPECTS)
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Alberta Stroke Program Early CT Score (ASPECTS) is a commonly used scoring system to select patients with stroke for endovascular treatment (EVT). However, the inter- and intra-reader variability is high. OBJECTIVE: To determine whether the inter- and intra-reader variability is different for various regions of the ASPECTS scoring system by evaluating the interobserver variability of ASPECTS between different readers in a per-region analysis. MATERIALS AND METHODS: All patients with acute ischemic stroke who proceeded to EVT in our institutions over a 4-year period were retrospectively identified from a prospectively maintained database. Images were reviewed by two experienced neuroradiologists, who recalculated the ASPECTS independently. We examined each region of the ASPECTS system to evaluate agreement between the raters in each area. RESULTS: 375 patients were included. The median total ASPECTS was 9 (IQR 8-9). The most common region showing ischemic change was the insula, with the M6 region being least commonly affected. Overall interobserver agreement for ASPECTS using Cohen's κ was 0.56 (95% CI 0.51 to 0.61). The region with the highest agreement was the insula (κ=0.56; 0.48 to 0.64). The region with the lowest agreement was M3 (κ=0.34; 0.12 to 0.56). Agreement was relatively good when ASPECTS were dichotomized into 0-5 versus 6-10 (κ=0.66; 0.49 to 0.84). CONCLUSIONS: Substantial interobserver variability is found when calculating ASPECTS. This variability is region dependent, and practitioners should take this into account when using ASPECTS for treatment decisions in patients with acute stroke.
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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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".