Risk Factors Associated with Incident Vertebral Fractures in Steroid-treated Males with Duchenne Muscular Dystrophy
Bibliographic record
Abstract
PURPOSE: Prevention of fractures is an unmet need in glucocorticoid (GC)-treated Duchenne muscular dystrophy. This study explored factors associated with incident vertebral fractures (VFs) to inform future fracture prevention efforts. METHODS: VFs were evaluated prospectively at study baseline and 12 months on lateral spine radiographs in participants aged 4 to 25 years with Duchenne muscular dystrophy. Clinical factors were analyzed for their association with the change in Spinal Deformity Index (sum of the Genant-defined VF grades from T4 to L4) between baseline and 12 months. RESULTS: Thirty-eight males were evaluated (mean ± SD age at baseline 11.0 ± 3.6 years; mean ± SD GC duration at baseline 4.1 ± 3.1 years; 74% ambulatory). Nine of 38 participants (24%) had 17 incident VFs, of which 3/17 VFs (18%) were moderate/severe. Participants with 12-month incident VF had lower mean ± SD baseline lumbar spine areal bone mineral density Z-scores (-2.9 ± 1.0 vs -1.9 ± 1.1; P = .049) and lower total body less head areal bone mineral density Z-scores (-3.1 ± 1.2 vs -1.6 ± 1.7; P = .036). Multivariable linear regression showed that at least 1 VF at baseline (P < .001), a higher number of antecedent non-VF (P < .001), and greater bone age delay at baseline (P = .027) were significant predictors of an increase in the Spinal Deformity Index from baseline to 12 months. CONCLUSION: The observation that ≥ 1 prevalent VF and/or non-VF were the strongest predictors of incident VFs at 12 months supports the need for prevention of first fractures in this high-risk setting. Bone age delay, a marker of GC exposure, may assist in the prioritization of patients in efforts to prevent first fractures.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".