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P3438The relationship between peak VO2 and each frailty-related factor. - Insight into frailty cycle

2017· article· en· W2761847548 on OpenAlexaboutno aff
Masamitsu Sugie, Kazumasa Harada, Marina Nara, Tetsuya Takahashi, Hajime Fujimoto, Takeo Koyama, Shunei Kyo, Hideki Ito

Bibliographic record

VenueEuropean Heart Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicHydrogen's biological and therapeutic effects
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGerontology

Abstract

fetched live from OpenAlex

Background: Frailty (not only physical frailty but also cognitive frailty) and frailty-related diseases (sarcopenia and cachexia) have much attention in recent years in cardiology, because they are known as important factors, which are related to life prognosis and cardiovascular mortality. Fried et al valued peak VO2 in frailty cycle. Peak VO2 is known as an index of exercise tolerance and/or cardiac output under max exercise, and moreover, peak VO2 is known as an index of life prognosis. However, the relationship between each frailty-related factor and peak VO2 has not been clear so far. Purpose: In this study, we aimed to determine the relationship between each frailty-related factor and peak VO2. Methods: One-hundred ninety four community dwelling older people are recruited from outpatients in our hospital (64 males, 130 females, age 78±6.6 years old). All subjects were measured with skeletal muscle mass Index (SMI), grip strength, usual walking speed (u-WS: m/s), timed up & go test (TUG), Montreal cognitive assessment with Japanese version (MoCA-J), geriatric depression scale (GDS), as well as C-reactive protein (CRP), hemoglobin (Hb), and serum-albumin (Alb), which biomarkers are included in the criteria of cachexia. In addition, all subjects underwent cardiopulmonary exercise test with symptom-limited to measure peak VO2.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.143
GPT teacher head0.367
Teacher spread0.224 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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