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Record W3174343567 · doi:10.2147/oarrr.s282606

Understanding and Managing Corticosteroid-Induced Osteoporosis

2021· review· en· W3174343567 on OpenAlexaff
Alexandra Kobza, Deena Herman, Αλεξάνδρα Παπαϊωάννου, Arthur Lau, Jonathan D. Adachi

Bibliographic record

VenueOpen Access Rheumatology Research and Reviews · 2021
Typereview
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsImpactMcMaster University
Fundersnot available
KeywordsOsteoporosisCorticosteroidMedicineInternal medicine

Abstract

fetched live from OpenAlex

Glucocorticoids are effective immunosuppressants used in a wide variety of diseases. Their use results in secondary osteoporosis in about 30-50% of chronic glucocorticoid users. Glucocorticoids cause a rapid decline in bone strength within the first 3-6 months mostly due to increased bone resorption by osteoclasts. This is followed by a more gradual loss of bone partly due to decreased osteoblastogenesis and osteoblast and osteocyte apoptosis. The loss of bone strength induced by glucocorticoids is not fully captured by bone mineral density measurements. Other tools such as the trabecular bone score and advanced imaging techniques give insight into bone quality; however, these are not used widely in clinical practice. Glucocorticoid-induced osteoporosis should be seen as a widely preventable disease. Currently, only about 15% of chronic glucocorticoid users are receiving optimal care. Glucocorticoids should be prescribed at the lowest dose and shortest duration. All patients should be counselled on lifestyle measures to maintain bone strength including nutrition and weight-bearing exercise. Pharmacological therapy should be considered for all patients at moderate to high risk of fracture as there is evidence for the prevention of bone loss and fractures with a favourable safety profile. Oral bisphosphonates are the current mainstay of therapy, whereas osteoanabolic agents may be considered for those at highest risk of fracture.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.901
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.006
Research integrity0.0010.003
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 head0.692
GPT teacher head0.590
Teacher spread0.103 · 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 designNot applicable
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

Citations70
Published2021
Admission routes1
Has abstractyes

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