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Record W3021440556 · doi:10.1161/str.51.suppl_1.2

Abstract 2: French Acute Cerebral Multimodal Imaging to Select Patients for Mechanical Thrombectomy Final Results

2020· article· en· W3021440556 on OpenAlexaff
Jean‐Marc Olivot, Jean François Albucher, Adrien Guenego, Michael Mlynash, Igor Sibon, Alain Viguier, Thomas Tourdias, Lionel Calvière, Fabrice Bonneville, Amel Drif, Nicolas Raposo, Jean Darcourt, Sören Christensen, Vanessa Rousseau, Anne Christine Januel, Mikaël Mazighi, Patrice Ménégon, Agnès Sommet, Claire Thalamas, Gregory W. Albers, Christophe Cognard

Bibliographic record

VenueStroke · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsCollège Lionel Groulx
Fundersnot available
KeywordsMedicinePenumbraProspective cohort studyNuclear medicineStroke (engine)Magnetic resonance imagingRadiologyInternal medicineIschemia

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.003

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.

Opus teacher head0.022
GPT teacher head0.284
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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