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Record W4413677730 · doi:10.1016/j.echo.2025.07.007

Clinical Applications of Strain Echocardiography: A Clinical Consensus Statement From the American Society of Echocardiography Developed in Collaboration With the European Association of Cardiovascular Imaging of the European Society of Cardiology

2025· article· en· W4413677730 on OpenAlexaffabout
James D. Thomas, Thor Edvardsen, Theodore P. Abraham, Vinesh Appadurai, Luigi P. Badano, José Banchs, Goo‐Yeong Cho, Bernard Cosyns, Victoria Delgado, Erwan Donal, Maurizio Galderisi, Roberto M. Lang, Thomas H. Marwick, Luc Mertens, Margaret Park, Bogdan A. Popescu, Zoran Popović, Marielle Scherrer‐Crosbie, Partho P. Sengupta, Sanjiv Shah, Peter Søgaard, Masaaki Takeuchi, Frank Weidemann, Jens‐Uwe Voigt

Bibliographic record

VenueJournal of the American Society of Echocardiography · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersTOMTEC Imaging SystemsGE Healthcare
KeywordsMedicineInternal medicineCardiologyClinical cardiologyConsensus conferenceCanadian Cardiovascular SocietyMyocardial infarction

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.057
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.057
Threshold uncertainty score0.299

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0570.051
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0050.003
Science and technology studies0.0020.005
Scholarly communication0.0060.003
Open science0.0070.004
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.0020.002

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 head0.018
GPT teacher head0.317
Teacher spread0.300 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations63
Published2025
Admission routes2
Has abstractno

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