Vitamin D supplementation for children with cancer: A systematic review and consensus recommendations
Bibliographic record
Abstract
BACKGROUND: Prevalent vitamin D deficiency (VDD) and low bone mineral density (BMD) have led to vitamin D supplementation for children with cancer, regardless vitamin D status. However, it remains unsettled whether this enhances bone strength. We sought to address this issue by carrying out a systematic review of the literature. METHODS: We conducted a literature search using PubMed, Embase, and Cochrane databases. Studies including children up to 5 years after cancer therapy were assessed for the association between 25-hydroxyvitamin D (25OHD) levels and BMD Z-scores or fractures, and the effect of vitamin D supplementation on BMD or fractures. Evidence quality was assessed using the GRADE methodology. RESULTS: Nineteen studies (16 observational and 3 interventional, mainly involving children with hematologic malignancies) were included. One study which analyzed 25OHD as a threshold variable (≤10 ng/ml) found a significant association between 25OHD levels and BMD Z-scores, while 25OHD as a continuous variable was not significantly associated with BMD Z-scores in 14 observational studies. We found neither a significant association between lower 25OHD levels and fractures (2 studies), nor between vitamin D (and calcium) supplementation and BMD or fracture frequency (3 studies) (very low quality evidence). CONCLUSION: There is a lack of evidence for an effect of vitamin D (and calcium) supplementation on BMD or fractures in children with cancer. Further research is needed; until then, we recommend dietary vitamin D/calcium intake in keeping with standard national guidelines, and periodic 25OHD monitoring to detect levels <20 ng/ml. Vitamin D/calcium supplementation is recommended in children with low levels, to maintain levels ≥20 ng/ml year-long.
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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 | 0.024 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".