Abstract 2: French Acute Cerebral Multimodal Imaging to Select Patients for Mechanical Thrombectomy Final Results
Bibliographic record
Abstract
Introduction: Target mismatch (TMM) identifies salvageable penumbra independent of time from stroke onset. Current guidelines do not recommend advanced imaging to select patients for mechanical thrombectomy (MT) within 6 hours after onset but indicate that more research is needed. To address this question, we designed a prospective multicenter cohort study to compare the rate of functional neurological recovery (mRS ≤2 @ 3 months) in patients treated by MT for ICA/M1/M2 occlusions within 6 hours after onset according to the presence of a TMM on baseline imaging. Hypothesis: 60% of patients with TMM vs. 35% of no TMM, would achieve an mRS≤2 at 3 months. Sample size calculation: 200 patients. Methods: Consecutive patients eligible for MT within 6 hrs after onset, who underwent CTP or DWI/PWI imaging before treatment were enrolled. No NIHSS or ASPECTS restrictions were applied. Treating teams were blinded of CTP/DWI/PWI maps. mRS at 3 months was rated by an investigator blinded to clinical/imaging/treatment information. Automatically processed maps by RAPID software were reviewed after the end of follow-up. TMM definition followed EXTEND-IA criteria: MM volume >10mL, MM ratio>1.2, Core volume <70 mL. Mismatch (MM) was defined by MM ratio>1.2 and MM volume>10 mL. Imaging-based subgroups (TMM vs. No TMM) were defined after the end of follow-up. Results: 218 patients were enrolled. Baseline imaging profile distribution was 71% TMM, 29% no TMM, (in the no TMM group, 76% had a core volume > 70 mL); 82% MM and 18% no MM. Reperfusion(TICI 2B-3) was achieved in 86% of the patients after a median delay of 4.4 hrs (95%CI 3.6-5.9). 61% of the patients in the TMM group vs. 35% in the no TMM group had an mRS ≤2 @ 3 months, p<0.001 (adjustment for age, onset to reperfusion, NIHSS, reperfusion and baseline imbalances). Reperfusion vs. no reperfusion was associated with an increased rate of good outcome in the TMM and MM groups (61% vs. 38% p=0.039 and 60% vs. 32%, p=0.016) but not in the no TMM or No MM groups (35% vs. 33%, NS; 35 vs. 45%., NS). Conclusion: Patients with salvageable penumbra on advanced imaging experienced a larger benefit from MT than those without. Patients with no penumbra did not appear to benefit from reperfusion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.014 | 0.003 |
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".