Energy metabolism, body composition, and urea generation rate in hemodialysis patients
Bibliographic record
Abstract
Abstract Hemodialysis ( HD ) adequacy is currently assessed using normalized urea clearance ( Kt / V ), although scaling based on Watson volume ( V ) may disadvantage women and men with low body weight. Alternative scaling factors such as resting energy expenditure and high metabolic rate organ mass have been suggested. The relationship between such factors and uremic toxin generation has not been established. We aimed to study the relationship between body size, energy metabolism, and urea generation rate. A cross‐sectional cohort of 166 HD patients was studied. Anthropometric measurements were carried on all. Resting energy expenditure was measured by indirect calorimetry, fat‐free mass by bio‐impedance and total energy expenditure by combining resting energy expenditure with a questionnaire‐derived physical activity data. High metabolic rate organ mass was calculated using a published equation and urea generation rate using formal urea kinetic modeling. Metabolic factors including resting energy expenditure, total energy expenditure and fat‐free mass correlated better with urea generation rate than did Watson volume. Total energy expenditure and fat‐free mass (but not Watson Volume) were independent predictors of urea generation rate, the model explaining 42% of its variation. Small women ( V ) had a significantly higher urea generation rate per kg than women with higher V . Similarly urea generation rate normalized to fat‐free mass was significantly greater in small women than in all others (significant only in comparison to larger men). Exercise‐related energy expenditure correlated significantly with urea generation rate. Energy metabolism, body composition and physical activity play important roles in small solute uremic toxin generation in HD patients and hence may impact on minimum dialysis requirements. Small women generate relatively more small solute toxins than other groups and thus may have a higher relative need for dialysis.
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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 | 0.000 | 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) | 0.001 | 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".