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Record W4324056747 · doi:10.3390/curroncol30030248

Vitamin D and Multiple Myeloma: A Scoping Review

2023· review· en· W4324056747 on OpenAlexaffvenue
Naghmeh Mirhosseini, Athanasios Psihogios, Meagan D. McLaren, Dugald Seely

Bibliographic record

VenueCurrent Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsOttawa HospitalKingston Health Sciences CentreUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineVitamin D and neurologyCalcitriol receptorCochrane LibraryMultiple myelomaDiseaseMEDLINEvitamin D deficiencyIncidence (geometry)Systematic reviewWarrantInternal medicineIntensive care medicineMeta-analysisBioinformatics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.621
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.412
GPT teacher head0.561
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations17
Published2023
Admission routes2
Has abstractyes

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