Factors Influencing Sarcopenic Changes in YUBI-WAKKA Finger-Ring Test Results After One Year: A Retrospective Observational Study
Bibliographic record
Abstract
Background: The YUBI-WAKKA (finger-ring) test was developed and validated as a predictor of sarcopenia, physical disability, and even mortality. We focused on the sarcopenic status and subsequent changes after 1 year using this test and analyzed factors related to these changes. We also examined the robustness of this test by administering it alongside annual checkups held in local clinics. Methods: We conducted the study to investigate the data of the annual checkup of National Health Insurance at primary care clinics in 2017 and 2018 in Tama City. We studied the participants in a retrospective observational way. The participants of the YUBI-WAKKA test were a total of 5,405 and 4,391 residents between the ages of 65 and 74 years. We first compared anthropological biomarkers of two groups, those that had calves larger than their own finger-ring and those that had calves smaller than their own finger-ring. Then, we compared these results to those from the previous year's 1,048 pairs of data. We were particularly interested in changes from larger to smaller, which we defined here as a sarcopenic change, and performed multivariate logistic regression analysis with forced entry methods to determine the related factors. We also calculated the concordant rate of the test results after 1 year. Results: In total, 14% (men) and 16% (women) each year received a positive (sarcopenic) YUBI-WAKKA test result. The factors related to this sarcopenic change in 1 year were low uric acid in men and a low or decreasing body mass index and increased hemoglobin and alanine aminotransferase in women. The concordant rates of the larger, just fit and smaller groups following finger-ring testing were 72.8%, 63.5%, and 52.0%, respectively. Conclusions: We propose that low uric acid and low or reducing body weight were related to low muscle mass and that ALT and Hb may be indirectly related to sarcopenia. These could be predictive factors to determine sarcopenia-prone individuals. The YUBI-WAKKA (finger-ring) test results and their change were considered to be a sustainable and reasonable way to use in the community with small effort to find those who are at high risk for sarcopenia among the elderly population and who may require intervention.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".