WCN25-427 THE BENEFITS OF ICODEXTRIN USE FOR CHILDREN RECEIVING CHRONIC PERITONEAL DIALYSIS: DATA FROM THE INTERNATIONAL PEDIATRIC PERITONEAL DIALYSIS NETWORK (IPPN) REGISTRY
Bibliographic record
Abstract
Introduction: Muscle wasting was previously reported to be associated with adverse clinical outcomes in peritoneal dialysis (PD) patients.Nonetheless, it is unknown whether early changes in muscle mass when patients are newly put on PD affect the subsequent clinical outcome.This study aims to understand factors affecting lean tissue mass changes over six months in PD patients and their prognostic significance.Methods: We studied 90 new PD patients.The change in lean tissue mass (LTM) and adipose tissue mass (ATM) over 6 months, as measured by bioimpedance spectrometry, were recorded.Outcome measures included patient, technical, and peritonitis-free survival rates.Results: After PD for 6 months, body weight and body mass index remained static.However, there was a significant decrease in LTM (38.6 Æ 9.9 to 37.7 Æ 9.3 kg, paired Student's t-test, p ¼ 0.041) and lean tissue percentage (LTMp) (63.4 Æ 13.6% to 61.5 Æ 13.4%, p ¼ 0.006), with a concomitant increase in ATM.Multiple linear regression models showed that the change in LTM and ATM are closely correlated; every 1 kg increase of ATM is associated with 1.01 kg decrease in LTM (95% confidence interval 0.797 to 0.855, p <0.0001).The change in LTM during the first 6 months of PD, however, was not associated with patient, technical, or peritonitisfree survival subsequently.Conclusions: During the first months of PD, reduction in LTM and concomitant increase in ATM are common.Our data suggest that muscle mass is being replaced by adipose tissue.However, the change in LTM during this period was not associated with adverse clinical outcomes subsequently.I have no potential conflict of interest to disclose.I did not use generative AI and AI-assisted technologies in the writing process.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".