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Record W2921501112 · doi:10.1177/2333794x19835661

Increased Rates of Vitamin D Insufficiency in Boys With Duchenne Muscular Dystrophy Despite Higher Vitamin D <sub>3</sub> Supplementation

2019· article· en· W2921501112 on OpenAlexafffund
Qian Bian, Laura McAdam, Marc D. Grynpas, Jane Mitchell, Jennifer Harrington

Bibliographic record

VenueGlobal Pediatric Health · 2019
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsHospital for Sick ChildrenLunenfeld-Tanenbaum Research InstituteSickKids FoundationHolland Bloorview Kids Rehabilitation HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineDuchenne muscular dystrophyMuscular dystrophyVitamin D and neurologyInternal medicinePediatricsEndocrinologyCardiology

Abstract

fetched live from OpenAlex

Vitamin D supplementation is important for many chronic pediatric conditions to help maintain bone health; however, there is little evidence about how disease-related factors affect vitamin D status. The objective was to compare 25-hydroxyvitamin D (25(OH)D) concentrations in 3 pediatric cohorts (Duchenne muscular dystrophy [DMD], systemic lupus erythematosus [SLE], and osteogenesis imperfecta [OI]). In a retrospective study of 367 subjects, children with DMD had increased prevalence of vitamin D insufficiency (25% vs 14% [SLE] and 10% [OI], P = .002), despite higher vitamin D 3 supplementation doses. Boys with DMD also had higher weight, fat mass, and lower lean mass percentage Z scores. DMD was associated with having higher rates of vitamin D insufficiency than other comparable pediatric chronic disease cohorts, the effect of which may be modulated by clinical factors such as increased adiposity. While corroboration of these results is needed given baseline differences between the patient groups, greater vitamin D supplementation doses may be required to achieve optimal serum 25(OH)D concentrations in boys with DMD.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.302
Teacher spread0.290 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations18
Published2019
Admission routes2
Has abstractyes

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