Volume Overload Assessed by Vector Impedance Analysis Is Associated with Poor Physical Function in Hemodialysis
Bibliographic record
Abstract
Background: Loss of skeletal muscle mass in chronic kidney disease (CKD) is linked to reduced physical function (PF). While this is well established, the role of volume overload (VO) common in hemodialysis (HD) patients remains underexplored. Methods: A total of 52 HD patients were included. MS was assessed using hand dynamometry. PF was measured using the Short Physical Performance Battery (SPPB), with "low PF" defined as a score ≤ 8. BIVA was graphed according to PF and MS classification as normal or reduced. The comparison of BIVA between groups was performed using Hotelling's T test. In addition, multivariate logistic regression (MLR) was used to explore the association between VO, MS, and PF. Results: BIVA graphs showed that patients with reduced PF were outside the 95th percentile, indicating VO, while those with normal PF had adequate hydration status (p=0.000). Only men with reduced MS had VO (p=0.003) (Figure 1). In Model 1 of the MLR, VO, age, and MS were associated with low PF; however, in Model 2, MS and diabetes remained significant predictors of low PF, while VO lost statistical significance (Table 1). Conclusion: HD patients with reduced PF and MS exhibited higher VO. Although VO is associated with worse PF. Funding: Private Foundation SupportTable 1. Multivariate Logistic Regression Model: Association Between Volume Overload, Physical Function, and Muscle Strength in Hemodialysis Patients - Variables Poor Physical Function(SPPB < 8) Model 1 OR IC 95 % Sex (m o f) 0.398 0.039 4.060 Age (years) 0.917 0.847 0.993 VO (yes/no) 0.086 0.007 0.990 Muscle strength (good/poor) 1.292 1.045 1.597 Model 2 OR IC95% Sex (m o f) 0.478 0.027 8.607 Age (years) 0.939 0.846 1.042 VO (yes/no) 0.258 0.015 4.533 Muscle strength (good/poor) 1.703 1.131 2.565 Diabetes (yes/no) 0.007 0.000 0.460
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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 | 0.003 |
| 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 | 0.001 | 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".