Musculoskeletal impairments in children receiving intensive therapy for acute leukemia or undergoing hematopoietic stem cell transplant: A report from the Children's Oncology Group
Bibliographic record
Abstract
BACKGROUND: Children receiving intensive chemotherapy for leukemia or undergoing hematopoietic stem cell transplant (HSCT) for solid tumors or leukemia are at risk for musculoskeletal (MSK) impairment from their underlying disease and from treatment. Data are limited on the incidence and nature of these disorders during intensive therapy. This study's objective was to provide a cross-sectional description of MSK impairments in this population. PROCEDURE: Children with acute myeloid leukemia (AML), relapsed acute lymphoblastic leukemia (rALL), or undergoing HSCT were systematically assessed for MSK impairments as part of Children's Oncology Group study ACCL0934. Assessments occurred at study entry, at 2 months, and at 12 months and included evaluation for signs or symptoms of MSK impairment and the type, site, and diagnosis. RESULTS: Six hundred three patients were included. MSK signs or symptoms were present in 48 (8.0%) children at study entry, 64 (13.5%) children at 2 months, and 40 (11.6%) children at 12 months. Arthralgia and/or gait abnormalities were the most common impairments; the knee was the most common site. Arthritis and tendonitis were both rare. Vincristine neuropathy, MSK impacts from central nervous system pathology, and bone or joint pain from underlying cancer were the most common diagnoses. Multivariate analysis demonstrated that having rALL (odds ratio [OR] 2.00, 95% CI 1.07-3.76, p = .03) or obesity (OR 2.10, 95% CI 1.12-3.95, p = .02) were risk factors for MSK impairment at study entry. CONCLUSIONS: MSK impairments are common in this intensively treated patient population, especially in those with rALL and those who are obese.
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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 | 0.001 | 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 | 0.001 |
| 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".