ASPECTS discrepancies between CT and MR imaging: analysis and implications for triage protocols in acute ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Optimal imaging triage for intervention for large vessel occlusions remains unclear. MR-based imaging provides ischemic core volumes at the cost of increased imaging time. CT Alberta Stroke Program Early CT Score (ASPECTS) estimates are faster, but may be less sensitive. OBJECTIVE: To assesses the rate at which MRI changed management in comparison with CT imaging alone. METHODS: Retrospective analysis of patients with acute ischemic stroke undergoing imaging triage for endovascular therapy was performed between 2008 and 2013. Univariate and multivariate analyses were performed. Multivariate logistic regression was used to evaluate the effect of time on disagreement in MRI and CT ASPECTS scores. RESULTS: A total of 241 patients underwent both diffusion-weighted imaging (DWI) and CT. Six patients with DWI ASPECTS ≥6 and CT ASPECTS <6 were omitted, leaving 235 patients. For 47 patients, disagreement between the two modalities resulted in different treatment recommendations. The estimated probability of disagreement was 20.0% (95% CI 15.4% to 25.6%). In a multivariate logistic regression, CT ASPECTS >7 (p=0.004) and admission National Institutes of Health Stroke Scale (NIHSS) score <16 (p=0.008) were simultaneously significant predictors of agreement in ASPECTS. The time between modalities was a marginally significant predictor (p=0.080). CONCLUSIONS: The study suggests that patients with NIHSS scores at admission of <16 and patients with CT ASPECTS >7 have a higher likelihood of agreement between CT and DWI based on an ASPECTS cut-off value of 6. Additional MRI for triage in patients with NIHSS at admission of >16, and ASPECTS of 6 or 7 may be more likely to change management. Unsurprisingly, patients with low CT ASPECTS had good correlation with MRI ASPECTS.
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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 | 0.026 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".