Ruling Out Brain CT Contraindications prior to Intravenous Thrombolysis: Diagnostic Equivalence between a Primary Interpretation Workstation and a Mobile Tablet Computer
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the equivalence of brain CT interpretations performed using a diagnostic workstation and a mobile tablet computer, in a telestroke service. MATERIALS AND METHODS: The ethics committee of our institution approved this retrospective study. A factorial design with 1452 interpretations was used. The assessed variables were the type of stroke classification, the presence of contraindications to the tPA administration, the presence of a hyperdense intracranial artery sign (HMCA), and the Alberta Stroke Program Early CT Score (ASPECTS) score. These variables were evaluated to determine the effect that the reading system had on their magnitudes. RESULTS: The achieved distribution of observed lesions using both the reading systems was not statistically different. The differences between the two reading systems to claim equivalence were 1.6% for hemorrhagic lesions, 4.5% for cases without lesion, and 5.2 for overall ischemic lesion. Equivalence was achieved at 2.1% for ASPECTS ≤ 6, 6.5% for the presence of imaging contraindication to the tPA administration, and 7.2% for the presence of HMCA. CONCLUSION: The diagnostic performance for detecting acute stroke is likely equivalent whether a tablet computer or a diagnostic workstation is used or not.
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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 | 0.017 | 0.112 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".