Body Composition (BC) in Octogenarians with CKD Stages 1-5ND: Role of Gender
Bibliographic record
Abstract
Background: The aging global population is rising CKD prevalence.BC changes are frequent in octogenarians, often intensifying negative effects. Data on CKD prevalence and BC clinical significance are limited. This study evaluates BC differences in octogenarians within a Nephrology Unit over 14 years, focusing on gender. Methods: 724 included (34.1%) of 2126 pts,335 women, 46.4%, age 84.7 ± 3.5 years, 42.1% diabetics underwent bioimpedance BIVA (Akern, Modena, Italy). Measurements Na-K exchange, Body Cell Mass (BCM), Muscle Mass (MM), Fat Mass (FM), Total Body Water (TBW), Intracellular Water (ICW), Extracellular Water (ECW), and Phase Angle (PhA).Muscle strength assessed with Handgrip Dynamometer (NexGen Ergonomics Inc, Quebec, Canada).Additionally, risk factors for RRT evaluated with the KFRE equation,fracture risk FRAX, and comorbidity with Charlson's age-adjusted algorithm. Data analyzed with SPSS 28.Significance P<0.05 Results: Epidemiologically, women exhibited lower comorbidity and risk of onset of RRT, but higher risk of osteoporotic fractures. BC, women had a higher % Fat 42.1±6.9 vs 31.8±6.9; p <0.001 and ECW% 52.6±6.02vs50.8±5.5;p <0.001,lower MM% 33.0±6.9 vs.39.4±7.12;P<0.001 and Na-K ex 1.08±0.2vs1.16±0.2;p <0.001.[Fig1] Females also showed lower muscle strength (16.5±4.9vs 26.1±7.4;p <0.001).Dynapenia was more common in men (27%) compared to women (20.4%).5.7% of patients exhibiting sarcopenia. Conclusion: Gender differs BC of octogenarians with CKD.Male have a high functional performance than women.Thus, tailored management therapies are essential to slow disease progression and improve quality of life. With the rising life expectancy, kidney transplantation could be a viable option for eligible patients. Funding: Private Foundation Support
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".