Genetic susceptibility and late bone outcomes in childhood acute lymphoblastic leukemia survivors
Bibliographic record
Abstract
Childhood acute lymphoblastic leukemia (cALL) survivors are at increased risk for bone comorbidities, but accurate screening tools for such comorbidities are limited. Polygenic scores (PGS) could stratify cALL survivors for risk of long-term adverse bone outcomes. We evaluated 214 (51% female) cALL survivors from the Prévenir les Effets TArdifs de la LEucémie study (median age 21 yr). Bone mineral density (BMD) measurements were obtained using dual X-ray absorptiometry at the lumbar spine (LS-BMD), femoral neck (FN-BMD), and total body (TB-BMD), and vertebral fractures (VF) were documented using the vertebral deformity criterion. We computed a PGS for adult heel quantitative ultrasound speed of sound (gSOS), known to be associated with the risk of osteoporotic fracture, using imputed genotype data of the participants, and tested it for association with BMD Z-scores and VF risk, adjusting for clinical risk factors, and in sex and prognostic risk-stratified analyses. We found that a gSOS below the mean was associated with lower BMD in all three sites in univariate and multivariate models. In univariate analyses, 1 SD increase in gSOS conferred a 0.16 SD increase in LS-BMD (95% CI 0.005-0.31), whereas a gSOS above the mean was associated with a 0.31 SD higher LS-BMD (95% CI 0.008-0.61), a 0.36 SD higher TB-BMD (95% CI 0.06-0.67), and a 0.43 SD higher FN-BMD (95% CI 0.13-0.72). Models combining gSOS with clinical risk factors explained up to 16% of the variance of BMD phenotypes and obtained an area under the receiver operating characteristic curve for VF of 0.77 in subgroup analyses. Cranial radiation, high cumulative glucocorticoid doses, high risk group, and male sex were significant risk factors for lower BMD Z-scores. In conclusion, a PGS, in combination with clinical risk factors, could be used as a tool to risk stratify cALL survivors for treatment-related bone morbidity.
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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 | 0.000 | 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".