Longitudinal follow-up of bone mineral density in children with nephrotic syndrome and the role of calcium and vitamin D supplements
Bibliographic record
Abstract
BACKGROUND: We previously have demonstrated that children with idiopathic nephrotic syndrome (INS) are at risk of metabolic bone disease (MBD). In this study, we report the longitudinal follow-up of these children and the role of calcium and vitamin D supplements. METHODS: We prospectively studied 100 consecutive children with INS. They were treated with prednisone. All were subjected to a baseline clinical, biochemical and radiological evaluation. They were initiated on calcium (500 mg/day) and vitamin D3 (200 IU/day) supplements, followed by a repeat assessment. The primary outcome measure was the Deltaz score (difference between the initial and final z scores) on dual energy X-linked absorptiometry (DEXA). A univariate and multivariate analysis using stepwise linear regression was performed for factors predictive of an improved Deltaz score. RESULTS: Of the 88 children that completed the study, the majority (n = 54) had improved bone mineral density (BMD) at the spine, and another 25 children had stable BMD on calcium and vitamin D3 supplements. The mean spinal BMD values were significantly better on follow-up (0.607+/-0.013 g/cm2) as compared with baseline values (0.561+/-0.010 g/cm2) (P<0.0001). The interval between initial and follow-up assessment was 1.5+/-0.07 years. Children who were on these supplements (n = 73) had a significantly improved z score as compared with those who did not receive them (n = 15) (P = 0.008). On multivariate analysis, the factors predictive of an improved z score were: younger age (P<0.0001), calcium and vitamin D3 supplement (P<0.0001), greater dietary calcium intake (P = 0.022) and lower interval steroid dose (P = 0.001). CONCLUSIONS: Children with greater steroid doses were likely to have low BMD on follow-up. Calcium and vitamin D supplements may help in improving BMD in children with INS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".