Modified Alberta Stroke Program Early CT Score (ASPECTS) of Contrast Extravasation on Dual-Energy CT Predicts Haemorrhagic Transformation and Poor Outcome After Endovascular Thrombectomy
Bibliographic record
Abstract
Purpose: Haemorrhagic transformation (HT) is an unpredictable complication of acute ischaemic stroke with large vessel occlusion following endovascular thrombectomy (EVT), and imaging parameters that are correlated with haemorrhage are unknown. We developed a modified version of the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) by adding a periventricular region to assess cerebral contrast extravasation (CE) on dual-energy computed tomography (DECT) and assessed its predictive value for HT. Methods: In total, 101 patients who underwent DECT immediately after EVT were prospectively enrolled. CE was defined as incident hyperdensity on iodine overlay maps. We quantified the CT attenuation in Hounsfield units (HU) and iodine concentration within the CE regions. The modified ASPECTS divided the middle cerebral artery vascular territory into 11 regions and added one region (paraventricular) to the original score. CE was scored as 1 point for each region, and the cumulative score was determined. Follow-up imaging was performed within 7 days postoperatively to confirm the occurrence of HT. A receiver operating characteristic (ROC) curve was constructed to assess the predictive value of various DECT-measured parameters for HT. Results: Overall, 75/101 (74.3%) patients exhibited CE following EVT, and 47/101 (46.5%) patients exhibited HT. In the ROC curve analysis, the DECT parameter with the maximal area under the curve (AUC) for HT was the modified ASPECTS (AUC=0.87), indicating that patients with a modified ASPECTS >2 were more likely to develop HT (sensitivity: 83.0%, specificity: 83.3%). The maximum iodine concentration (AUC=0.76) and maximum CT attenuation (AUC=0.68) in the hyperdense region were also predictors of postoperative HT. Conclusion: The modified ASPECTS is a practical and sensitive method for assessing postoperative HT risk in patients following EVT.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".