MétaCan
Menu
Back to cohort
Record W6947769019 · doi:10.3929/ethz-b-000351636

Cardiac MRI Endpoints in Myocardial Infarction Experimental and Clinical Trials: JACC Scientific Expert Panel

2019· article· en· W6947769019 on OpenAlexfundno aff

Bibliographic record

VenueRepository for Publications and Research Data (ETH Zurich) · 2019
Typearticle
Languageen
FieldChemistry
TopicWood and Agarwood Research
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteAbbott VascularBoston Scientific CorporationCircle Cardiovascular ImagingCentro Nacional de Investigaciones CardiovascularesMinisterio de Ciencia e InnovaciónSiemens HealthineersUniversity of GlasgowNational Institutes of HealthEdwards LifesciencesGlaxoSmithKlineAmerican College of Cardiology FoundationAstraZeneca
KeywordsMyocardial infarctionCardiac magnetic resonanceClinical trialSelection (genetic algorithm)Clinical endpointEndpoint DeterminationMagnetic resonance imaging

Abstract

fetched live from OpenAlex

After a reperfused myocardial infarction (MI), dynamic tissue changes occur (edema, inflammation, microvascular obstruction, hemorrhage, cardiomyocyte necrosis, and ultimately replacement by fibrosis). The extension and magnitude of these changes contribute to long-term prognosis after MI. Cardiac magnetic resonance (CMR) is the gold-standard technique for noninvasive myocardial tissue characterization. CMR is also the preferred methodology for the identification of potential benefits associated with new cardioprotective strategies both in experimental and clinical trials. However, there is a wide heterogeneity in CMR methodologies used in experimental and clinical trials, including time of post-MI scan, acquisition protocols, and, more importantly, selection of endpoints. There is a need for standardization of these methodologies to improve the translation into a real clinical benefit. The main objective of this scientific expert panel consensus document is to provide recommendations for CMR endpoint selection in experimental and clinical trials based on pathophysiology and its association with hard outcomes.

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)0.271
metaresearch head score (Gemma)0.243
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.271
Threshold uncertainty score0.898

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2710.243
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.007
Bibliometrics0.0050.008
Science and technology studies0.0020.002
Scholarly communication0.0100.003
Open science0.0110.006
Research integrity0.0180.012
Insufficient payload (model declined to judge)0.0110.011

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 head0.358
GPT teacher head0.505
Teacher spread0.147 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations24
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueRepository for Publications and Research Data (ETH Zurich)Same topicWood and Agarwood ResearchFrench-language works237,207