Vitamin D and Multiple Myeloma: A Scoping Review
Bibliographic record
Abstract
As the global incidence of multiple myeloma (MM) increases, the identification of modifiable risk factors for disease prevention becomes paramount. Maintaining optimal vitamin D status is a candidate for prevention efforts, based on pre-clinical evidence of a possible role in disease activity and progression. A structured scoping review was performed to identify and describe human-level research regarding the association between vitamin D and MM risk and/or prognosis. Searches of three databases (OVID-Medline, OVID-Embase, and OVID-Cochrane Library) yielded 15 included publications. Vitamin D deficiency is fairly common among patients with MM, with 42.3% of participants in the studies identified as having a vitamin D deficiency. No included publication reported on vitamin D status and the risk of developing or being newly diagnosed with MM. Possible associations with vitamin D that warrant future exploration include the incident staging of MM disease, the occurrence of peripheral neuropathy, and survival/prognosis. Vitamin D receptor (VDR) polymorphisms associated with MM also warrant further investigation. Overall, this scoping review was effective in mapping the research regarding vitamin D and MM and may help support new hypotheses to better describe this association and to better address identified knowledge gaps in the literature.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".