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Record W2986261891 · doi:10.1016/j.echo.2019.08.012

Similarities and Differences in Left Ventricular Size and Function among Races and Nationalities: Results of the World Alliance Societies of Echocardiography Normal Values Study

2019· article· en· W2986261891 on OpenAlexaff
Federico M. Asch, Tatsuya Miyoshi, Karima Addetia, Rodolfo Citro, Masao Daimon, Sameer Desale, Pedro Gutiérrez Fajardo, Ravi R. Kasliwal, James N. Kirkpatrick, Mark Monaghan, Denisa Muraru, Kofo O. Ogunyankin, Seung Woo Park, Ricardo Ronderos, Anita Sadeghpour, G. Scalia, Masaaki Takeuchi, Wendy Tsang, Edwin S. Tucay, Ana Clara Tude Rodrigues, Vivekanandan Amuthan, Yun Zhang, Alexandra Blitz, Roberto M. Lang, Aldo Prado, Eduardo Filipini, Agatha Kwon, Samantha Hoschke-Edwards, Tânia Regina Afonso, Babitha Thampinathan, Maala Sooriyakanthan, Tiangang Zhu, Zhilong Wang, Yingbin Wang, Mei Zhang, Yu Zhang, Lixue Yin, Shuang Li, R Alagesan, Sowmya Balasubramanian, R.V.A. Ananth, Manish Bansal, Luigi P. Badano, Chiara Palermo, Eduardo Bossone, Davide Di Vece, Michele Bellino, Tomoko Nakao, Takayuki Kawata, Megumi Hirokawa, Naoko Sawada, Hye Rim Yun, Ji‐won Hwang, Dolapo Fasawe, Marcus Schreckenberg, Mei Zhang, V Amuthan, Pedro Gutiérrez‐Fajardo

Bibliographic record

VenueJournal of the American Society of Echocardiography · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsToronto General HospitalUniversity of Toronto
FundersMedStar Health Research InstituteUniversity of Chicago
KeywordsMedicineEjection fractionInterquartile rangeDemographyCardiologyInternal medicineHeart failure

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)0.002
metaresearch head score (Gemma)0.005
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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.229
Teacher spread0.220 · 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

Citations189
Published2019
Admission routes1
Has abstractno

Explore more

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