Normal anthropometry does not equal normal body composition in pediatric intestinal failure
Bibliographic record
Abstract
BACKGROUND: Published reports on abnormal body composition in pediatric patients with intestinal failure have been in patients with poor growth. The goal of the current study is to report the body composition of normally growing patients with intestinal failure. METHODS: Children 8-18 years old with a dual-energy x-ray absorptiometry (DXA) between January 1, 2013, and July 15, 2018, were included in the study. Data were retrospectively collected from the medical charts and included demographics, residual bowel anatomy, nutrition support, height, and weight. DXA data, including total body less head bone mineral density (BMD), fat mass (FM), and fat-free mass (FFM), were collected and compared with published literature controls matched for age and sex. RESULTS: Thirty-four children met inclusion criteria. Mean age at the time of DXA was 9.6 ± 1.8 years. Weight- and height-for-age z-scores were -0.4 ± 0.9 and -0.5 ± 1.0, respectively. Mean BMD z-score was -1.0 ± 1.3. Twenty-six percent of patients (n = 9) had reduced BMD. Patients with intestinal failure had higher FM (P = .02) and lower FFM (P = .02) compared with controls. CONCLUSIONS: These data show that, despite reference range z-scores for height and weight, children with intestinal failure are at risk for abnormal body composition. Body composition should be routinely measured in children with intestinal failure to direct nutrition interventions.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".