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
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Bibliographic record
Abstract
<h3>BACKGROUND AND PURPOSE:</h3> 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. <h3>MATERIALS AND METHODS:</h3> 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 <i>U</i> and Fisher exact tests. <h3>RESULTS:</h3> 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%, <i>P </i>= .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. <h3>CONCLUSIONS:</h3> 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it