Bibliographic record
Abstract
ndovascular thrombectomy (EVT) has transformed outcomes for stroke due to large vessel occlusion.However, this intervention may not be efficacious for all.Approximately 20% of large vessel occlusion have large infarct core on computer tomography or magnetic resonance imaging.These patients have been underrepresented or excluded in previous thrombectomy trials, and most guideline recommendations are not applicable to this group.The extent of ischemia can be quantified, using tools such as the Alberta Stroke Program Early CT Score (ASPECTS) for noncontrast computer tomography (where lower scores indicate greater tissue damage and a score of 5 or less is generally regarded as threshold for large infarct), or by measuring core volume using perfusion (computer tomography) or diffusion (magnetic resonance) imaging, in which a core of 50 mL or more is often considered to be indicative of large stroke.In this Synopsis, we consider 3 trials that challenge the assumption that thrombectomy may not be suitable for large acute infarcts.RESCUE-Japan LIMIT (Recovery by Endovascular Salvage for Cerebral Ultra-Acute Embolism Japan Large Ischemic Core Trial) assessed EVT for large ischemic stroke (Yoshimura et al.Endovascular therapy for acute stroke with a large ischemic region.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.125 | 0.082 |
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".