Predictors of low and very low bone mineral density in long‐term childhood acute lymphoblastic leukemia survivors: Toward personalized risk prediction
Bibliographic record
Abstract
BACKGROUND: Cohorts of childhood acute lymphoblastic leukemia (cALL) survivors reaching adulthood are increasing. Approximately 30% of survivors meet criteria for low bone mineral density (BMD) 10 years after diagnosis. We investigated risk factors for low BMD in long-term cALL survivors. METHODS: We recruited 245 cALL survivors from the PETALE (Prévenir les effets tardifs des traitements de la leucémie aiguë lymphoblastique chez l'enfant) cohort, who were treated with the Dana Farber Cancer Institute protocols, did not experience disease relapse or hematopoietic stem cell transplants, and presented with more than 5 years of event-free survival. Median time since diagnosis was 15.1 years. RESULTS: Prevalence of low DXA-derived BMD (Z-score ≤-1) ranged between 21.9% and 25.3%, depending on site (lumbar spine (LS-BMD), femoral neck (FN-BMD), and total body (TB-BMD), and between 3.7% and 5.8% for very low BMD (Z-score ≤-2). Males had a higher prevalence of low BMD than females for all three outcomes (26%-32% vs. 18%-21%), and male sex acted as a significant risk factor for low BMD in all models. Treatment-related factors such as cumulative glucocorticoid (GC) doses and cranial radiation therapy (CRT) were associated with lower BMDs in the full cohort and in females at the FN-BMD site. CONCLUSION: Low and very low BMD is more prevalent in male cALL survivors. Male sex, high cumulative GC doses, CRT, risk group, and low body mass index (BMI) were identified as risk factors for low BMD. A longer follow-up of BMD through time in these survivors is needed to establish if low BMD will translate into a higher risk for fragility fractures through adulthood.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".