The Prediction of Lean Body Mass and Fat Mass From Arm Anthropometry at Diagnosis in Children With Cancer
Bibliographic record
Abstract
Maintenance of adequate nutrition is important in the care of children with cancer. In clinical practice, determination of nutritional status can be accomplished with measurement of body composition by dual-energy x-ray absorptiometry (DXA). However, DXA is seldom available in low-income countries where most children with cancer live. This study sought to provide predictive equations for lean body mass and fat mass, measured by DXA, on the basis of simple arm anthropometry providing measures of mid-upper arm circumference and triceps skin-fold thickness in a population (N=99) of children diagnosed with cancer. Such equations were derived successfully with the inclusion of absolute body weight, the body weight Z-score, and the predicted whole-body bone mineral content on the basis of age and sex. Attempted validation in a small sample (N=7) of children who completed therapy for acute lymphoblastic leukemia revealed disparities reflective of the prevalence of obesity in such survivors. Further validation must be undertaken in large samples of children with a variety of malignant diseases to assess the robustness of the equations predictive of body composition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".