Prediction of Hemorrhage after Successful Recanalization in Patients with Acute Ischemic Stroke: Improved Risk Stratification Using Dual-Energy CT Parenchymal Iodine Concentration Ratio Relative to the Superior Sagittal Sinus
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Brain parenchymal hyperdensity on postthrombectomy CT in patients with acute stroke can be due to hemorrhage and/or contrast staining. We aimed to determine whether iodine concentration within contrast-stained parenchyma compared with an internal reference in the superior sagittal sinus on dual-energy CT could predict subsequent intracerebral hemorrhage. MATERIALS AND METHODS: Seventy-one patients with small infarct cores (ASPECTS ≥ 7) and good endovascular recanalization (modified TICI 2b or 3) for anterior circulation large-vessel occlusion were included. Brain parenchymal iodine concentration as per dual-energy CT and the percentage of contrast staining relative to the superior sagittal sinus were recorded and correlated with the development of intracerebral hemorrhage using Mann-Whitney U and Fisher exact tests. RESULTS: Forty-three of 71 patients had parenchymal hyperdensity on initial dual-energy CT. The median relative iodine concentration compared with the superior sagittal sinus was significantly higher in those with subsequent intracerebral hemorrhage (137.9% versus 109.2%, P = .007). By means of receiver operating characteristic analysis, a cutoff value of 100% (iodine concentration relative to the superior sagittal sinus) enabled identification of patients going on to develop intracerebral hemorrhage with 94.75% sensitivity, 43.4% specificity, and a likelihood ratio of 1.71. CONCLUSIONS: Within our cohort of patients, the relative percentage of iodine concentration at dual-energy CT compared with the superior sagittal sinus was a reliable predictor of intracerebral hemorrhage development and may be a useful imaging biomarker for risk stratification after endovascular treatment.
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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 | 0.000 | 0.002 |
| 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".