The use of e-ASPECTS in acute stroke care: validation of method performance compared to the performance of specialists
Bibliographic record
Abstract
BACKGROUND: The rapid evaluation of non-contrast-enhanced computed tomography (NCCT) brain scans in patients with anterior stroke symptoms saves time and favors optimal and prompt treatment. e-ASPECTS is a tool that automatically calculates the Alberta Stroke Program Early CT Score (ASPECTS) values, leading to a more accurate and timely image evaluation. OBJECTIVE: To determine the ability of e-ASPECTS in differentiating images with and without injury. METHODS: One-hundred sixteen patients admitted to a stroke unit in a Brazilian tertiary hospital underwent a CT scan at admission and at least one control brain imaging (NCCT or magnetic resonance imaging - MRI) 24 hours after admission. ASPECTS evaluation was performed by three neuroradiologists, three neurologists, and three neurology residents, all blinded to the symptoms and the injury side. The scores were compared to the ground truth, and an ASPECTS score was provided by two independent non blinded evaluators. Sensitivity and specificity were analyzed, and receiver operating characteristic curves, Bland-Altman plots with mean error score, and Matthews correlation coefficients (MCCs) were obtained for ASPECTS scores, assuming values equal to 10 for images without injury and values other than 10 for images with ischemic injury. RESULTS: e-ASPECTS demonstrated similar performance to that of neuroradiologists and neurologists, with an area under the curve of 0.78 and an MCC value of 0.48 in the dichotomous analysis. The sensitivity and specificity of e-ASPECTS were 75% and 73%, respectively. CONCLUSION: e-ASPECTS is a validated and reliable tool for determining early signs of ischemia in NCCT.
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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 | 0.046 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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".