Is There a Difference in Body Composition of Autologous Hematopoietic Stem Cell Transplantation Recipients on Enteral versus Parenteral Nutritional Support? A Pilot Study
Bibliographic record
Abstract
ABSTRACT Background Parenteral nutrition (PN) is the current standard of nutritional care for autologous hematopoietic stem cell transplantation (AHSCT) recipients. However, the American Society for Parenteral and Enteral Nutrition recommends using enteral nutrition (EN), because PN is more expensive and associated with higher rates of infection. Currently, there is minimal evidence examining body composition (BC) in AHSCT recipients on EN versus those on PN. Our study aimed to determine differences in BC, including muscle mass, phase angle, percent body fat, and muscle strength, in these 2 groups of patients. Methods Thirty-five AHSCT recipients were randomized to receive EN or PN. Participants were followed at baseline and 15 days and 30 days post-transplantation. Bioelectrical impedance analysis was used to measure fat mass (FM), lean body mass (LBM), and phase angle (PhA). The Z-fat-free mass index (FFMI) was calculated. Ultrasound was used to assess quadriceps muscle layer thickness (QMLT), and a dynamometer evaluated hand grip strength (HGS). Population means were used to calculate standardized PhA (SPhA). The t tests and χ 2 test were performed with SPSS software. A P value <.05 was considered to indicate statistical significance. Results The study cohort comprised 16 EN patients and 19 PN patients, with a mean age of 61.2 ± 9.66 years. There were no significant differences between the groups in QMLT, FM, z-FFMI, HGS, SPhA, and LBM at baseline, day 15, or day 30. Conclusion AHSCT recipients on EN had similar body composition to those on PN, providing convincing evidence that EN may be an acceptable and less expensive nutritional modality in AHSCT recipients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".