Sarcopenia in Children With Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Children with acute lymphoblastic leukemia experience musculoskeletal morbidity during therapy. We examined the patterns of change in skeletal muscle mass (SMM) and the relationship between change in SMM and the burden of illness as reflected in days of hospitalization. Ninety-one children had dual energy x-ray absorptiometry (DXA scans) during treatment, yielding the sum of lean tissue mass in all 4 limbs; the appendicular lean mass. SMM was derived from appendicular lean mass. The number of inpatient days was recorded. DXA scans at 5 time points showed a profile of change in SMM characterized by a drop in the mean Z score from -0.18 at diagnosis to -1.08 after 6 months of therapy, with a partial recovery 12 to 24 months after diagnosis. Levels of serum creatinine, a surrogate measure of SMM, were mainly unchanged. The extent of the drop in SMM during early therapy was associated with the duration of hospitalization (r=0.31, P<0.05). Children with acute lymphoblastic leukemia experience a notable reduction in SMM early in treatment, with incomplete recovery. The degree of loss is associated with the burden of illness. These findings provide a target for a therapeutic intervention and a measure to determine its efficacy.
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".