Serum 24,25-Dihydroxyvitamin D Concentrations in Osteogenesis Imperfecta: Relationship to Bone Parameters
Bibliographic record
Abstract
BACKGROUND: Several studies suggest that 24,25-dihydroxyvitamin D [24,25(OH)₂D] may have an effect on bone mass and metabolism. OBJECTIVE: We evaluated the relationship between serum 24,25(OH)₂D levels and bone density and bone metabolism in children with a primary bone disorder-osteogenesis imperfecta (OI). MATERIALS AND METHODS: The study included 132 patients (age, 1.1 to 17.9 yr; 67 girls) with OI types I, III, or IV who had not received bisphosphonate treatment at the time of analysis. RESULTS: Serum 24,25(OH)₂D levels were significantly higher in OI type III than in OI type I or IV. Serum 24,25(OH)₂D concentrations were positively correlated with serum 25-hydroxyvitamin D (25OHD) levels and negatively correlated with serum PTH levels, and were not correlated with serum 1α,25-dihydroxyvitamin D [1,25(OH)₂D]. The ratio between serum 24,25(OH)₂D and 25OHD was negatively correlated with age and was independent of serum 25OHD concentrations. Regression analysis revealed that OI severity (P = 0.04), serum 25OHD levels (P < 0.001), and serum PTH concentrations (P = 0.045), but not age, gender, or serum 1,25(OH)₂D, were independent predictors of serum 24,25(OH)₂D levels. No correlation was found between serum 24,25(OH)₂D levels or the ratio between serum 24,25(OH)₂D and 25OHD and lumbar spine bone mineral density z-scores or bone marker levels (serum osteocalcin and urinary collagen type I N-telopeptide) after adjusting for OI type, age, and gender. CONCLUSION: Patients with more severe OI type had higher 24,25(OH)₂D serum levels and higher serum 24,25(OH)₂D to 25OHD ratios, suggesting an increased 25OHD-24-hydroxylase activity.
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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".