Hyper-attenuating brain lesions on CT after ischemic stroke and thrombectomy are associated with final brain infarction
Bibliographic record
Abstract
Purpose Hyper-attenuating lesions, or contrast staining, on a non-contrast brain computed tomography (NCCT) scan have been investigated as a predictor for hemorrhagic transformation after endovascular treatment of acute ischemic stroke (AIS). However, the association of hyper-attenuating lesions and final ischemic areas are poorly investigated in this setting. The aim of the present study was to assess correlations between hyper-attenuating lesions and final brain infarcted areas after thrombectomy for AIS. Methods Data from patients with AIS of the anterior circulation who underwent endovascular treatment were retrospectively assessed. Images of the brain NCCT scans were analyzed in the first hours and late after treatment. The hyper-attenuating areas were compared to the final ischemic areas using the Alberta Stroke Program Early CT Score (ASPECTS). Results Seventy-one of the 123 patients (65.13%) treated were included. The association between the hyper-attenuating region in the post-thrombectomy CT scan and final brain ischemic area were sensitivity (58.3% to 96.9%), specificity (42.9% to 95.6%), positive predictive values (71.4% to 97.7%), negative predictive values (53.8% to 79.5%), and accuracy values (68% to 91%). The highest sensitivity values were found for the lentiform (96.9%) and caudate nuclei (80.4%) and for the internal capsule (87.5%), and the lowest values were found for the M1 (58.3%) and M6 (66.7%) cortices. Conclusions Hyper-attenuating lesions on head NCCT scans performed after endovascular treatment of AIS may predict final brain infarcted areas. The prediction appears to be higher in the deep brain regions compared with the cortical regions.
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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 | 0.003 |
| 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".