Predictors of Vertebral Deformity in Long-Term Survivors of Childhood Acute Lymphoblastic Leukemia: The PETALE Study
Bibliographic record
Abstract
BACKGROUND: The prevalence of vertebral deformities in long-term survivors of childhood acute lymphoblastic leukemia (ALL) is unknown. Our objectives were to identify the prevalence of vertebral deformities and their risk factors among long-term childhood ALL survivors. METHODS/RESULTS: We recruited 245 (49% male) long-term childhood ALL survivors from the Preventing Late Adverse Effects of Leukemia Cohort (French-Canadian ALL survivors treated between the years 1987 and 2010 with the Dana Farber Cancer Institute clinical trials protocols, who did not experience disease relapse and/or receive hematopoietic stem cell transplant). Median age at recruitment was 21.7 years (range, 8.5-41) and median time since diagnosis was 15.1 years (range, 5.4-28.2). All participants underwent spine radiograph and dual-energy X-ray absorptiometry scans. The prevalence of vertebral deformity was 23% with 88% classified as grade 1 according to the Genant method. The majority of vertebral deformities were clinically silent. Regression analysis confirmed male sex (risk ratio [RR] = 1.94; 95% confidence interval [CI], 1.16-3.24; P = 0.011), higher glucocorticoid cumulative dose (RR = 1.05; 95% CI, 1.00-1.10; P = 0.032), and back pain (RR = 2.44; 95% CI, 1.56-3.84; P < 0.001) as predictors of prevalent vertebral deformity. Sex differences in vertebral deformity predictors emerged. CONCLUSIONS: We report a significant prevalence of vertebral deformities in this young cohort. Male sex, cumulative glucocorticoid dose, and back pain were identified as predictors of prevalent vertebral deformity. Back pain emerging as a strong predictor of vertebral deformity underscores the importance of ongoing bone health surveillance in survivors with persistent vertebral deformities treated with these earlier protocols.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".