Can Total Body Resistance Measured Using Bioelectrical Impedance Analysis Be the Index of Dehydration in Older Japanese Patients?
Bibliographic record
Abstract
BACKGROUND: Dehydration in older patients has long been considered a significant health problem because it implies increased morbidity and mortality. However, dehydration is detected by a combination of physical signs and blood tests. For older people dwelling at home and in nursing homes, a simple and non-invasive method for detecting dehydration by caregivers is needed. The total body resistance is measured using bioelectrical impedance analysis and is known as an indicator of dehydration. There are no data from older Japanese patients on this issue. We performed this study to examine the relationship between dehydration and total body resistance in Japan. METHODS: We performed blood tests and measured bioelectrical impedance in older outpatients aged ≥ 65 years from the Internal Medicine Department at Mito Kyodo General Hospital. Patients were classified as dehydrated and non-dehydrated using the dehydration index with a blood urea nitrogen/creatinine ratio > 20, and the mean total body resistance was compared between the two groups. RESULTS: Eighty-one patients were recruited in the study. In the dehydrated group, the mean total body resistance was 439 Ω at 50 kHz, which was significantly higher than that in the non-dehydrated group (408 Ω, P = 0.038). CONCLUSIONS: The total body resistance measurements can be used for simple assessment of dehydration among older Japanese patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".