Assessment of body composition in pediatric patients with cystic fibrosis
Bibliographic record
Abstract
RATIONALE: Cystic fibrosis (CF) leads to pathological changes in organs that express the cystic fibrosis transmembrane conductance regulator (CFTR), including secretory cells of the digestive tract and the pancreas. Maintaining nutritional sufficiency is challenging for CF patients and therefore accurate monitoring is important for their clinical management. PURPOSE: The objectives of this study were to evaluate the effectiveness of skinfold measurements as an accurate method for determining body composition (fat mass (FM) and lean body mass (LBM)) of this population, using dual-energy X-ray absorptiometry (DEXA) as a gold standard comparison and to determine the most accurate equation for this calculation in children with CF. METHODS: Fifty-five pediatric patients with CF participated in the study. FM and LBM calculated via four methods: Slaughter, Durnin, Durenberg (2-site and 4-site). The relationship between the methods and DEXA results were estimated by intraclass-correlation coefficient (ICC) and Bland and Altman analyses. RESULTS: The Slaughter method was the most accurate (ICC of 0.92 for FM and 0.99 for LBM) and displayed the least bias over the range of FM and LBM in CF patients. In addition, the results of Bland Altman analyses comparing each skinfold method to DEXA, revealed that the results were evenly distributed along the range of values for the Slaughter calculation, whereas the other three methods under and over estimated % fat results at the upper and lower ends of the range respectively. CONCLUSION: We therefore conclude that the Slaughter method may be used for body composition assessment of pediatric CF patients. This provides clinical teams with a simple, accurate and non-invasive method that can be used to monitor nutritional status in pediatric patients with CF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".