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Record W2583593970 · doi:10.1016/j.echo.2016.10.016

The Clinical Use of Stress Echocardiography in Non-Ischaemic Heart Disease: Recommendations from the European Association of Cardiovascular Imaging and the American Society of Echocardiography

2017· article· en· W2583593970 on OpenAlexaff
Patrizio Lancellotti, Patricia A. Pellikka, Werner Budts, Farooq A. Chaudhry, Erwan Donal, Raluca Dulgheru, Thor Edvardsen, Madalina Garbi, Jong‐Won Ha, Garvan C. Kane, Joe Kreeger, Luc Mertens, Philippe Pîbarot, Eugenio Picano, Thomas Ryan, Jeane Mike Tsutsui, Albert Varga

Bibliographic record

VenueJournal of the American Society of Echocardiography · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecSickKids FoundationUniversité LavalHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineIschaemic heart diseaseStress EchocardiographyCardiologyInternal medicineDiseaseHeart diseaseIntensive care medicineRadiologyCoronary artery disease

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)0.030
metaresearch head score (Gemma)0.071
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.071
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0050.003
Science and technology studies0.0020.004
Scholarly communication0.0050.003
Open science0.0060.003
Research integrity0.0110.014
Insufficient payload (model declined to judge)0.0020.004

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 head0.020
GPT teacher head0.303
Teacher spread0.284 · 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
GenreReview

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

Citations307
Published2017
Admission routes1
Has abstractno

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