Value of high-density sign on CT images after mechanical thrombectomy for large vessel occlusion in predicting hemorrhage and unfavorable outcome
Bibliographic record
Abstract
Purpose Cerebral hyperdensities can appear on head computed tomography (CT) images performed early after endovascular treatment (EVT) in patients with acute ischemic stroke and may be secondary to contrast staining or hemorrhagic transformation. The aim of this study was to determine how the high-density sign on CT affects mortality and clinical outcome and whether CT parameters predict hemorrhagic conversion or unfavorable outcome. Methods We retrospectively reviewed a database of patients who underwent EVT with mechanical thrombectomy for acute ischemic stroke over 7 years. Included were acute stroke patients with a CT examination within 24 h post-EVT with mechanical thrombectomy, demonstrating areas of hyperdensity. We evaluated morphologic characteristics of these lesions, location, CT Hounsfield units and largest area, as well as patient demographics, EVT methods and patient outcome. Results A total of 29 patients met the strict inclusion criteria. Complete recanalization was achieved in 58.6% (17/29). Seventeen (58.6%) cases of post-intervention cerebral hyperdensities were related to contrast staining and 12 (41.4%) cases to contrast staining and hemorrhage. Patient mortality was significantly higher in the hemorrhagic group (50.0% versus 5.9%, p = 0.003). The increased density on CT was associated with higher hemorrhagic risk (odds ratio 1.05, p = 0.036). Conclusion Patients with the high-density sign on CT images after mechanical thrombectomy for acute ischemic stroke demonstrated increased mortality and worse clinical outcome, primarily when these hyperdensities were related to hemorrhage. CT imaging parameters as higher density areas can help in the differentiation of hemorrhage from contrast staining.
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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 | 0.005 |
| 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".