Exploring body composition in people with chronic kidney disease with and without obesity
Bibliographic record
Abstract
Background: In Canada, 23,000 individuals require dialysis for end-stage kidney disease [ESKD] many of whom have a high Body Mass Index [BMI]. Obesity is a barrier to transplantation, making alternative body composition parameters crucial for assessing eligibility. Background: In Canada, 23,000 individuals require dialysis for end-stage kidney disease [ESKD] many of whom have a high Body Mass Index [BMI]. Obesity is a barrier to transplantation, making alternative body composition parameters crucial for assessing eligibility. Purpose:To explore body composition (BC) metrics in patients with ESKD with higher BMI (≥30 kg/m²) versus lower BMI ( Methodology:A prospective cohort study with 92 kidney recipients stratified into lower BMI ( Results:The higher BMI group had greater mean LBM (70.14 ± 15.25 kg) and mean %BF (31.90 ± 8.27%), while the lower BMI group had higher mean nHGS (0.44 ± 0.17 kg/m²). HGS (21% vs. 3%, p = 0.027) and nHGS (55% vs. 17%, p Conclusion: These findings suggest that looking beyond BMI and considering overall body composition can provide clinicians a more robust medical picture of a patient’s health, helping them make more informed transplant decisions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".