P3438The relationship between peak VO2 and each frailty-related factor. - Insight into frailty cycle
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".