“Yubi-wakka” (Finger-Ring) Test: A Tool to Detect Prefrailty in Elderly Populations, a Pilot Study
Bibliographic record
Abstract
Background: Preventing frailty of elderly is an urgent issue in Japan. The “Yubi-wakka” (finger-ring) test was developed and validated as a predictor of sarcopenia, disability and even mortality. To clarify the prevalence of “frailty” defined by this test and the relationship between other indexes cross-sectionally and prospectively, we conducted this study. Methods: Five thousand four hundred and five subjects who were 65 to 74 years old participated in this study. In a sitting position, the subjects surrounded their calf using their own finger-ring, and whether the calf was larger, just fit, or smaller than the finger-ring was determined. We analyzed these “Yubi-wakka” (finger-ring) test results and other clinical indexes. We used Student’s t -tests and the Chi-squared tests to compare the data between the groups, and logistic regression tests to adjust for multiple variables. Results: In total, 38.8% of the subjects’ calves were judged as being “larger”, 45.6% as “just fit” and 15.6% as “smaller”, which was the positive test result. The positive rate differed among medical facilities without any known different characteristics. The comparison between the “larger” and “smaller” groups revealed that body weight, red blood cell count, serum lipids, uric acid and liver enzymes were significantly different between the groups. Metabolic syndrome was more common in the “larger” group. In multiple analysis, low body mass index was an independent risk factor in both sexes. Positive urinary glucose, higher aspartate aminotransferase, systolic blood pressure and low alanine transaminase were risk factors for positive test results for males. Smoking, high hemoglobin and old age were risk factors for positive test results in females. Conclusions: The test was simple and feasible enough for the primary care setting, without the requirement of any devices. However, the positive rate varied among the clinics. The subjects’ age was limited to under 75 years, and the test possibly detected individuals without metabolic syndrome and fatty liver. We are also planning to increase the subjects’ age range and collect data prospectively. J Clin Med Res. 2019;11(9):623-628 doi: https://doi.org/10.14740/jocmr3917
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".