Evaluation of Large Ischemic Cores to Predict Outcomes of Thrombectomy: A Proposal of a Novel Treatment Phase
Bibliographic record
Abstract
Background Endovascular treatment of large ischemic cores is challenging. The severity of ischemic stress is assessed using the apparent diffusion coefficient (ADC). We aimed to evaluate the ADC in patients with a low Alberta Stroke Program Early CT [Computed Tomography] Score using diffusion‐weighted imaging and whether it correlates with clinical outcomes. Methods This study included consecutive patients with acute large ischemic stroke (Alberta Stroke Program Early CT Score‐diffusion‐weighted imaging ≤5) who underwent endovascular treatment with successful recanalization between April 2014 and March 2023. The most frequent ADC (peak ADC) and diffusion‐weighted imaging lesion volumes were assessed. The primary outcome was the 3‐month modified Rankin Scale (mRS) score. Good (mRS score, 0–3) and poor clinical outcomes (mRS score, 4–6) were compared to confirm whether ADC was associated with clinical outcomes. Results In total, 78 patients were enrolled in this study; 30 had an mRS score of 0 to 3 at 3 months. The peak ADC in these patients was significantly higher than that in patients with mRS scores of 4 to 6 ( P = 0.0002). In multivariate analysis, peak ADC was strongly associated with good clinical outcomes (odds ratio, 1.231; P = 0.0135) rather than onset‐to‐recanalization time and ischemic core volume. The optimal peak ADC threshold for discriminating between the mRS groups was 520×10 −6 mm 2 /s with a sensitivity of 75% and a specificity of 73%. Good clinical outcomes were more frequently observed in patients with peak ADC ≥520×10 −6 mm 2 /s ( P <0.0001). Conclusion In large ischemic cores, diffusion‐weighted imaging lesions with peak ADCs ≥520×10 −6 mm 2 /s are associated with favorable outcomes. Evaluation of the ischemic core is necessary to confirm endovascular treatment.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".