Relationships of Bone Mineral Density to Whole Body Mass, Fat Mass and Fat-free Mass in Long-term Survivors of Acute Lymphoblastic Leukemia in Childhood
Bibliographic record
Abstract
Body size influences bone mineral density (BMD) in health. Relationships of BMD with body mass index, fat mass (FM), fat-free mass, and appendicular lean mass were explored in acute lymphoblastic leukemia (ALL) survivors (n=75; 41 males; 45 standard risk ALL) >10 years from diagnosis. Dual energy radiograph absorptiometry performed body composition analysis. Relationships were assessed by regression analyses and Pearson correlation coefficients (r). Twenty subjects (26.3%) were osteopenic; lumbar spine (LS) BMD Z score <-1.00. Age at diagnosis, sex, ALL risk-category, type of post-induction steroid or cranial radiation did not correlate with LS or whole body (WB) BMD. Body mass index correlated significantly with LS BMD (r=0.333, P=0.004) and WB BMD (r=0.271, P=0.033). FM index (FM/height²) Z score showed no significant correlation with LS or WB BMD. Fat-free mass index Z score correlated strongly with LS BMD (r=0.386, P=0.013) and WB BMD (r=0.605, P<0.001) in males but not in females. The appendicular lean mass index, a surrogate for skeletal muscle mass, correlated significantly with LS BMD (r=0.367, P=0.018) and WB BMD (r=0.604, P<0.001) in males but not in females. Future studies to evaluate interventions to enhance BMD focused on improving body composition particularly skeletal muscle mass are warranted.
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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 | 0.000 | 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".