Prediction of whole body composition utilizing cross-sectional abdominal imaging in pediatrics
Bibliographic record
Abstract
Abstract Background: Although body composition is an important determinant of pediatric health outcomes, we lack tools to routinely assess it in clinical practice. We define models to predict whole body skeletal muscle and fat composition, as measured by dual X-ray absorptiometry (DXA) or whole body magnetic resonance imaging (MRI), in pediatric oncology and healthy pediatric cohorts, respectively. Methods: Pediatric oncology patients (≥5 to ≤18 years) undergoing an abdominal CT were prospectively recruited for a concurrent study DXA scan. Cross-sectional areas of skeletal muscle and total adipose tissue at each lumbar vertebral level (L1-L5) were quantified and optimal linear regression models were defined. Whole body and cross-sectional MRI data from a previously recruited cohort of healthy children (≥5 to ≤18 years) was analyzed separately. Results: Eighty pediatric oncology patients (57% male; age range 5.1-18.4y) were included. Cross-sectional areas of skeletal muscle and total adipose tissue at lumbar vertebral levels (L1-L5) were correlated with whole body lean soft tissue mass (LSTM) ( R 2 =0.896-0.940) and fat mass (FM) ( R 2 =0.874-0.936) (p<0.001). Linear regression models were improved by the addition of height for prediction of LSTM (adjusted R 2 =0.946-0.971; p<0.001) and by the addition of height and sex (adjusted R 2 =0.930-0.953) (p<0.001)) for prediction of whole body FM. High correlation between lumbar cross-sectional tissue areas and whole body volumes of skeletal muscle and fat, as measured by whole body MRI, was confirmed in an independent cohort of 73 healthy children. Conclusion: Regression models can predict whole body skeletal muscle and fat in pediatric patients utilizing cross-sectional abdominal images.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".