ADC Threshold Indicating the Ischemic Region for Predicting Efficacy in Thrombectomy
Bibliographic record
Abstract
Abstract Purpose : The effectiveness of endovascular thrombectomy (EVT) has been proven in patients with large cerebral infarction. However, the size of the ischemic region before treatment is a significant factor in the outcome, and the optimal method for the evaluation of this region is uncertain. The goal of this study was to investigate apparent diffusion coefficient (ADC) values as a basis for an assessment of the ischemic region before treatment. Methods : A retrospective study was performed in 48 consecutive patients who underwent EVT for acute large vessel occlusion (LVO) with Alberta Stroke Program Early Computed Tomography Score (ASPECTS) ≤5 from 2014 to 2022. Associations of clinical characteristics and ADC-related ischemic region volumes with a favorable outcome (modified Rankin Scale (mRS) 0–3 at 90 days) were examined. Results : The 48 patients had a median age of 78 years and a median NIHSS score of 23 at admission. Occlusion sites were the internal carotid artery (46%), M1 segment (46%), and M2 segment (8%). Specifically, 18 cases (38%) were mRS 0–3 and 30 (62%) mRS 4–6 at 90 days. In receiver operating characteristic (ROC) analysis, an ischemic region defined as a volume with an ADC < 540 (ADC 540 ) had the highest area under the curve (AUC) value (AUC = 0.85). Multivariate analysis showed independent associations between onset to reperfusion time (OR 0.991, 95% CI 0.981–1.000, p = 0.013) and ADC 540 (OR 0.887, 95% CI 0.807–0.976, p = 0.001) with mRS 0–3 at 90 days. Conclusions : Earlier reperfusion and a smaller ischemic region defined by ADC 540 were related to a favorable outcome in patients with acute LVO with a large ischemic region.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".