Reliability and accuracy of individual Alberta Stroke Program Early CT Score regions using a medical and a smartphone reading system in a telestroke network
Bibliographic record
Abstract
Introduction The aim of this study was to assess individual regions of the Alberta Stroke Program Early CT Score in noncontrast head computed tomography interpretations using a smartphone in a telestroke network, by comparison to a medical monitor. Methods The review board of our institution approved this retrospective study. A factorial design with 188 patients, four radiologists and two reading systems was used. Accuracy and reliability were evaluated. Results Very good interobserver agreements were observed on the total Alberta Stroke Program Early CT Score for both the medical and smartphone reading systems, with intraclass correlation coefficients of 0.91 and 0.84 respectively. Interobserver agreements were moderate to very good for the medical reading system (all intraclass correlation coefficients >0.74), whereas they were fair to very good for the smartphone (intraclass correlation coefficients ranged from 0.31–0.81). All intraobserver agreements were good (intraclass correlation coefficient >0.64), except for internal capsule (0.48) and M2 (0.55) regions. The areas under the receiver-operating curve ranged from 0.69–0.89 for the medical system, while for the smartphone ranged from 0.44–0.86. No statistical differences were observed between medical and smartphone reading systems for each region (all p > 0.05). Discussion If radiologists are better trained in the evaluation of the lesions in the insula, the internal capsule and the M2 regions, the total and the dichotomised Alberta Stroke Program Early CT Score will be more precise. Hence, ruling out contraindications to thrombolysis administration will be improved, allowing assessment of head computed tomography in a telestroke network using a smartphone to be a common practice.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".