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Record W2150905436 · doi:10.1155/2013/638727

Metabolic and Clinical Consequences of Hyperthyroidism on Bone Density

2013· article· en· W2150905436 on OpenAlexaff
Jagoda Gorka, Regina M. Taylor‐Gjevre, Terra Arnason

Bibliographic record

VenueInternational Journal of Endocrinology · 2013
Typearticle
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineOsteoporosisSubclinical infectionHormoneThyroidDiseaseBone remodelingAntithyroid drugsThyroid diseaseMetabolic bone diseaseInternal medicineEndocrinologyPhysiologyPediatricsGraves' disease

Abstract

fetched live from OpenAlex

In 1891, Von Recklinghausen first established the association between the development of osteoporosis in the presence of overt hyperthyroidism. Subsequent reports have demonstrated that BMD loss is common in frank hyperthyroidism, and, to a lesser extent, in subclinical presentations. With the introduction of antithyroid medication in the 1940s to control biochemical hyperthyroidism, the accompanying bone disease became less clinically apparent as hyperthyroidism was more successfully treated medically. Consequently, the impact of the above normal thyroid hormones in the pathogenesis of osteoporosis may be presently underrecognized due to the widespread effective treatments. This review aims to present the current knowledge of the consequences of hyperthyroidism on bone metabolism. The vast number of recent papers touching on this topic highlights the recognized impact of this common medical condition on bone health. Our focus in this review was to search for answers to the following questions. What is the mechanisms of action of thyroid hormones on bone metabolism? What are the clinical consequences of hyperthyroidism on BMD and fracture risk? What differences are there between men and women with thyroid disease and how does menopause change the clinical outcomes? Lastly, we report how different treatments for hyperthyroidism benefit thyroid hormone-induced osteoporosis.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.029
GPT teacher head0.341
Teacher spread0.312 · 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

Citations77
Published2013
Admission routes1
Has abstractyes

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