The Influence of Body Composition on Bone Health in Long-term Survivors of Acute Lymphoblastic Leukemia in Childhood and Adolescence: Analyses by Dual-energy Radiograph Absorptiometry and Peripheral Quantitative Computed Tomography
Bibliographic record
Abstract
BACKGROUND: The normal interrelationship of body composition with bone health is less clear in the context of disease. Survivors of acute lymphoblastic leukemia (ALL) exhibit sarcopenic obesity and osteopenia. The impact of body composition on bone health in such survivors was examined. SUBJECTS AND METHODS: Survivors of ALL (N=74), >10 years from diagnosis, underwent dual-energy radiograph absorptiometry and peripheral quantitative computed tomography. RESULTS: Whole-body bone mineral content (WB BMC) Z scores were greater in males than females, but WB BMC indices (WB BMC/height 2 ) were comparable (0.74±0.125 and 0.72±0.069, respectively). WB BMC index (I) and fat-free mass index correlated significantly with trabecular bone mineral density, only in males. Fat mass index and appendicular lean mass index showed no such correlations. WB BMCI and fat-free mass index also correlated, again predominantly in males, with measures of strength in both trabecular and cortical bone. WB BMCI also correlated strongly with trabecular number, thickness, and hole size, also only in males. CONCLUSIONS: The results point to the need for enhancing muscle mass, measured by appendicular lean mass index, while reducing fat mass and maintaining good bone mineralization in long-term survivors of ALL to ensure the integrity of healthy bones.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".