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Record W4410550806 · doi:10.1016/j.jacasi.2025.03.011

Epidemiology of Valvular Heart Disease in Asia Pacific Region

2025· review· en· W4410550806 on OpenAlexaff
Chak-Yu So, Jonathan Yap, Guangyuan Song, Karl Poon, Shih‐Hsien Sung, Mann Chandavimol, Kentaro Hayashida, Duk‐Woo Park, See-Hooi Ewe, Mi Chen, Juri Iwata, Tarinee Tangcharoen, Paul Jie Wen Tern, Han-Su Park, Mirvat Alasnag, Yohei Ohno, Jimmy Kim Fatt Hon, Rohan Bhagwandeen, Minoru Tabata, Alex Pui‐Wai Lee, Hasan Jilaihawi, Dee Dee Wang, Gilbert H.L. Tang, D. Scott Lim, Thomas Modine, Yat-Yin Lam

Bibliographic record

VenueJACC Asia · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEpidemiologyvalvular heart diseaseDiseaseMedicineCardiologyAsia pacificInternal medicineGeographyBusinessInternational trade

Abstract

fetched live from OpenAlex

Valvular heart disease poses a significant health burden in the Asia-Pacific region, with its epidemiology varying widely across countries caused by diverse socioeconomic and health care situations. Rheumatic heart disease remains prevalent, especially in low- to middle-income areas, while degenerative valvular diseases are emerging in developed regions caused by an aging population. Significant disparities in access to health care and intervention result in variable clinical outcomes. In the past decade, transcatheter interventions have revolutionized the management of patients with valvular heart disease globally. In the Asia-Pacific region, the uptake and development of transcatheter valvular interventions has been slow until recent years. Continued collaboration across the Asia-Pacific region is essential to mitigate the impact of the upcoming surge of valvular heart disease in this diverse and rapidly changing area.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.056
GPT teacher head0.427
Teacher spread0.371 · 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

Citations12
Published2025
Admission routes1
Has abstractyes

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