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Record W2808397645

Management of bone health in patients with celiac disease: Practical guide for clinicians.

2018· article· en· W2808397645 on OpenAlexaff
Donald R. Duerksen, María Inés Pinto-Sánchez, Alexandra Anca, Joyce Schnetzler, Shelley Case, Jenni Zelin, Adrianna Smallwood, Justine Turner, Elena F. Verdú, J. Decker Butzner, Mohsin Rashid

Bibliographic record

VenuePubMed · 2018
Typearticle
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsMcMaster UniversityCanadian Celiac Association
Fundersnot available
KeywordsMedicineOsteoporosisVitamin D and neurologyMalabsorptionBone healthBone mineralDiseaseMetabolic bone diseaseBone densityPediatricsVitaminCalciumInternal medicineIntensive care medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe clinical issues related to bone health in patients with celiac disease (CD) and to provide guidance on monitoring bone health in these patients. SOURCES OF INFORMATION: A PubMed search was conducted to review literature relevant to CD and bone health, including guidelines published by professional gastroenterological organizations. MAIN MESSAGE: Bone health can be negatively affected in both adults and children with CD owing to the inflammatory process and malabsorption of calcium and vitamin D. Most adults with symptomatic CD at diagnosis have low bone mass. Bone mineral density should be tested at diagnosis and at follow-up, especially in adult patients. Vitamin D levels should be measured at diagnosis and annually until they are normal. In addition to a strict gluten-free diet, supplementation with calcium and vitamin D should be provided and weight-bearing exercises encouraged. CONCLUSION: Bone health can be adversely affected in patients with CD. These patients require adequate calcium and vitamin D supplementation, as well as monitoring of vitamin D levels and bone mineral density with regular follow-up to help prevent osteoporosis and fractures.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.034
GPT teacher head0.359
Teacher spread0.325 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations27
Published2018
Admission routes1
Has abstractyes

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