Interobserver Reliability of Baseline Noncontrast CT Alberta Stroke Program Early CT Score for Intra-Arterial Stroke Treatment Selection
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Early ischemic changes on pretreatment NCCT quantified using ASPECTS have been demonstrated to predict outcomes after IAT. We sought to determine the interobserver reliability of ASPECTS for patients with AIS with PAO and to determine whether pretreatment ASPECTS dichotomized at 7 would demonstrate at least substantial κ agreement. MATERIALS AND METHODS: From our prospective IAT data base, we identified consecutive patients with anterior circulation PAO who underwent IAT over a 6-year period. Only those with an evaluable pretreatment NCCT were included. ASPECTS was graded independently by 2 experienced readers. Interrater agreement was assessed for total ASPECTS, dichotomized ASPECTS (≤ 7 versus >7), and each ASPECTS region. Statistical analysis included determination of Cohen κ coefficients and concordance correlation coefficients. PABAK coefficients were also calculated. RESULTS: One hundred fifty-five patients met our study criteria. Median pretreatment ASPECTS was 8 (interquartile range 7-9). Interrater agreement for total ASPECTS was substantial (concordance correlation coefficient = 0.77). The mean ASPECTS difference between readers was 0.2 (95% confidence interval, -2.8 to 2.4). For dichotomized ASPECTS, there was a 76.8% (119/155) observed rate of agreement, with a moderate κ = 0.53 (PABAK = 0.54). By region, agreement was worst in the internal capsule and the cortical areas, ranging from fair to moderate. After adjusting for prevalence and bias, agreement improved to substantial or near perfect in most regions. CONCLUSIONS: Interobserver reliability is substantial for total ASPECTS but is only moderate for ASPECTS dichotomized at 7. This may limit the utility of dichotomized ASPECTS for IAT selection.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".