P3.48: Body composition of pediatric patients with intestinal failure
Bibliographic record
Abstract
Introduction: Infants and children with intestinal failure (IF) are at risk of growth failure and altered body composition with increased fat and decreased lean mass compared to healthy children. Data from our cohort of patients with IF show normal growth on growth charts. However, body composition has not yet been assessed. The goal of the current study was to compare body composition of patients with IF treated by our program to healthy children using the United States National Health statistic database. Methods: We conducted a retrospective cohort study of patients referred to our program between January 1st 2013 and July 15th 2018. For routine clinical monitoring, all patients with IF have annual Dual-energy x-ray absorptiometry (DXA) to assess bone mass. All patients with a DXA within the timeframe of the study and aged 8–18 years were included. Data related to demographics, residual bowel anatomy, nutritional support and growth anthropometrics were collected. Statistical analysis included means with SD for continuous variables and frequencies with percentages for categorical variables. Height, weight, fat, lean and bone mass were converted to their respective z-scores; regression analysis assessed predictors of body composition. Results: Thirty-seven patients met inclusion criteria and a total of 68 DXA results were collected. The mean age at the time of the DXA was 10.7±2.2 years. Subjects demonstrated normal growth with weight and height z-scores of -0.67±0.99 and -0.7 ±1.3, respectively. Lean and fat mass z-scores were -1.61 ±1.09 and 0.24± 0.74. Z-score for total body less head (TBLH), bone mineral density (BMD) and bone mineral content (BMC) were -1.19 ±1.37, -0.9 ±1.08 and -0.86 ±1.29 in the lumbar spine (LS). Small bowel length predicted 38% of the change in BMD in the LS. Linear growth was the most important predictor of BMC in the TBLH. There was a positive relationship between weight z-scored and fat mass z-scores (p=0.01) and a trend towards increased fat mass with longer time on parenteral nutrition (PN) (p = 0.09). Conclusions: The results suggest normal growth and body composition in our patients with IF. This suggests that patients with IF have potential to accomplish normal body composition during growth. Further research is needed in the younger age group as well as separating those on and off PN. It is also important to determine positive contributors to body composition to increase efficiency of care in this population.
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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 | 0.001 | 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) | 0.003 | 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".