Alberta Stroke Program Early CT Score (ASPECTS) in Real Time: A Prospective Study of its Interobserver Variation among Radiologists in a Training Hospital from a Low-Resource Setting
Bibliographic record
Abstract
Background and Objectives. The Alberta Stroke Program Early CT Score (ASPECTS) is a standardized system used to quantify the extent of ischemic involvement in cases of acute middle cerebral artery (MCA) infarct. It aids in clinical decision-making to identify patients who are more likely to benefit from thrombolytic therapy. This study aimed to determine the interobserver variation of ASPECTS among training and expert radiologists in a real-time, low-resource setting. Patients and Methods. A prospective study was conducted on non-enhanced CT (NECT) images of 79 patients with acute stroke. Patients with hemorrhagic stroke, or ischemic stroke from territories other than the MCA, were excluded. The ASPECTS of each case was assessed by three groups of radiologists—residents, fellows, and an expert. The level of agreement among them was then analyzed. Results. ASPECT scores were dichotomized into >7 and ≤7. With the expert’s reading as gold standard, residents had sensitivity of 0.94 [95% CI: 0.85, 0.99] and specificity of 0.68 [95% CI: 0.46, 0.85], while fellows had sensitivity of 0.96 [95% CI: 0.87, 1.00] and specificity of 0.76 [95% CI: 0.55, 0.91]. There was substantial agreement between residents and expert in overall ASPECTS rating (Κ = 0.66 [95% CI: 0.48, 0.85]; AC1 = 0.77 [95% CI: 0.62, 0.91]), and substantial to almost perfect agreement between fellows and expert (Κ = 0.76 [95% CI: 0.59, 0.92]; AC1 = 0.83 [95% CI: 0.71, 0.95]). Differences between the ASPECTS of the expert and trainees were within 2 points in most cases. Conclusion. ASPECTS is a reliable tool for both training and expert radiologists to quantify the extent of MCA infarcts. Assessment by trainees is comparable with that of the expert reader and is useful for immediate clinical decision making in low-resource settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".