Predictors of bony morbidity in children with acute lymphoblastic leukemia
Bibliographic record
Abstract
BACKGROUND: To evaluate the relationship between lumbar spine (LS) bone mineral density (BMD) and patient-, disease-, and therapy-related variables, and to define the risk-factors for fractures in children receiving therapy on Dana-Farber Cancer Institute acute lymphoblastic leukemia (ALL) protocols. METHODS: Children (≤18 years) diagnosed with ALL during the period 1995-2006, who are in first clinical remission, were included (n = 124). Dual-energy X-ray absorptiometry provided LS-BMD at diagnosis (n = 46) and during continuation therapy. LS-BMD was expressed as Z scores based on local population norms. Regression analyses evaluated the risk of osteopenia (Z-score -1.01 to -1.99, osteoporosis (Z-score -2.00 or less) and fractures. RESULTS: At diagnosis, 14 0f 46 (30%) patients had osteopenia and 5 (11%) had osteoporosis; whereas, during continuation therapy, 47 of 124 (39.5%) patients had osteopenia, and 10 (8%) had osteoporosis. LS-BMD at diagnosis had a positive linear relationship with LS-BMD during continuation therapy (Pearson correlation coefficient 0.619, P < 0.0001). Multivariable analyses identified age ≥10 years and LS-BMD at diagnosis as independent predictors of LS-BMD during continuation therapy. Twenty-three (18.5%) patients developed fractures. Dexamethasone therapy (OR 3.4, 95% CI 1.31, 7.52, P = 0.01) and lower LS-BMD during the continuation therapy (OR 1.8, 95% CI 1.2, 2.8, P = 0.01) were independent predictors of fracture. CONCLUSIONS: Older age and lower LS-BMD at diagnosis are predictors of lower LS-BMD during continuation therapy. Dexamethasone and lower LS-BMD during continuation therapy are associated with fractures. Using these variables it is feasible to develop a predictor model to define the risk of bony morbidity in children receiving ALL therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".