Mode of Imaging Study and Endovascular Therapy for a Large Ischemic Core: Insights From the RESCUE-Japan LIMIT
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Differences in measurement of the extent of acute ischemic stroke using the Alberta Stroke Program Early Computed Tomographic Score (ASPECTS) by non-contrast computed tomography (CT-ASPECTS stratum) and diffusion-weighted imaging (DWI-ASPECTS stratum) may impact the efficacy of endovascular therapy (EVT) in patients with a large ischemic core. METHODS: The RESCUE-Japan LIMIT (Recovery by Endovascular Salvage for Cerebral Ultra-acute Embolism Japan-Large IscheMIc core Trial) was a multicenter, open-label, randomized clinical trial that evaluated the efficacy and safety of EVT in patients with ASPECTS of 3-5. CT-ASPECTS was prioritized when both CT-ASPECTS and DWI-ASPECTS were measured. The effects of EVT on the modified Rankin Scale (mRS) score at 90 days were assessed separately for each stratum. RESULTS: Among 183 patients, 112 (EVT group, 53; No-EVT group, 59) were in the CT-ASPECTS stratum and 71 (EVT group, 40; No-EVT group, 31) in the DWI-ASPECTS stratum. The common odds ratio (OR) (95% confidence interval) of the EVT group for one scale shift of the mRS score toward 0 was 1.29 (0.65-2.54) compared to the No-EVT group in CT-ASPECTS stratum, and 6.15 (2.46-16.3) in DWI-ASPECTS stratum with significant interaction between treatment assignment and mode of imaging study (P=0.002). There were significant interactions in the improvement of the National Institutes of Health Stroke Scale score at 48 hours (CT-ASPECTS stratum: OR, 1.95; DWIASPECTS stratum: OR, 14.5; interaction P=0.035) and mortality at 90 days (CT-ASPECTS stratum: OR, 2.07; DWI-ASPECTS stratum: OR, 0.23; interaction P=0.008). CONCLUSION: Patients with ASPECTS of 3-5 on MRI benefitted more from EVT than those with ASPECTS of 3-5 on CT.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".