Early computed tomography-based scores to predict decompressive hemicraniectomy after endovascular therapy in acute ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Identification of patients requiring decompressive hemicraniectomy (DH) after endovascular therapy (EVT) is crucial as clinical signs are not reliable and early DH has been shown to improve clinical outcome. The aim of our study was to identify imaging-based scores to predict the risk for space occupying ischemic stroke and DH. METHODS: Prospectively derived data from patients with acute large artery occlusion within the anterior circulation and EVT was analyzed in this monocentric study. Predictive value of non-contrast cranial computed tomography (ncCT) and cerebral blood volume (CBV) Alberta Stroke Program Early CT score (ASPECTS) were investigated for DH using logistic regression models and Receiver Operating Characteristic Curve analysis. RESULTS: From 218 patients with EVT, DH was performed in 20 patients (9.2%). Baseline- (7 vs. 9; p = 0.009) and follow-up ncCT ASPECTS (1 vs. 7, p<0.001) as well as baseline CBV ASPECTS (5 vs. 7, p<0.001) were significantly lower in patients with DH. ncCT (baseline: OR 0.71, p = 0.018; follow-up: OR 0.32, p = <0.001) and CBV ASPECTS (OR 0.63, p = 0.008) predicted DH. Cut-off ncCT-ASPECTS on baseline was 7-, ncCT-ASPECTS on follow-up was 4- and CBV ASPECTS on baseline was 5 points. CONCLUSIONS: ASPECTS could be useful to early identify patients requiring DH after EVT for acute large vessel occlusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".