Region‐specific interobserver agreement of the Alberta Stroke Program Early Computed Tomography Score: A meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Alberta Stroke Program Early CT Score (ASPECTS) is a widely used scoring system for evaluating ischemic stroke to determine therapeutic strategy. However, there is variation in the interobserver agreement of ASPECTS. This systematic review and meta-analysis aimed to investigate the interobserver agreement of total and regional ASPECTS. METHODS: A comprehensive search was conducted in the Web of Sciences, PubMed, and Scopus databases to identify relevant studies. Inclusion criteria were studies of noncontrast CT performed within 24 hours of ischemic stroke in the middle cerebral artery territory. RESULTS: A total of 20 studies, with 3482 patients, reporting interobserver agreement of total and regional ASPECTS were included in the meta-analysis. The interobserver agreement for total ASPECTS in studies using Kappa coefficient (κ) analysis was substantial (κ = .67, 95% confidence interval [CI]: .57-.78). In studies using intraclass correlation coefficient (ICC) analysis, agreement was excellent (ICC = .84, 95% CI: .77-.90). Interobserver agreement was higher in studies in which the observer was unblinded to clinical scenario in both groups (κ = .74, 95% CI: .59-.89, and ICC = .82, 95% CI: .79-.85). Per-region analysis showed that the caudate nucleus had the highest agreement (κ = .68, 95% CI: .60-.76, and ICC = .84, 95% CI: .74-.93), while M2 and internal capsule in Kappa studies (κ = .45, 95% CI: .34-.55 and κ = .47, 95% CI: .28-.66), and M4 and internal capsule in ICC studies (ICC = .54, 95% CI: .43-.64 and ICC = .55, 95% CI: .18-.91) had the lowest agreement. CONCLUSION: This meta-analysis demonstrates substantial to excellent interobserver agreement for total ASPECTS, which supports using this method for stroke treatment. However, findings emphasize the need to consider interobserver agreement in specific regions of ASPECTS for treatment decisions.
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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 | 0.043 | 0.079 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.059 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".