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 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 | 0.000 | 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 | 0.000 |
| 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".