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Record W2000686842 · doi:10.1155/2012/323061

Are Selective Serotonin Reuptake Inhibitors a Secondary Cause of Low Bone Density?

2012· article· en· W2000686842 on OpenAlexaff
Kim Chau, Stephanie A. Atkinson, Valerie H. Taylor

Bibliographic record

VenueJournal of Osteoporosis · 2012
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsWomen's College HospitalUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineOsteoporosisAntidepressantMEDLINEDiseaseDepression (economics)PsychiatryMajor depressive disorderIntensive care medicineInternal medicineAnxiety

Abstract

fetched live from OpenAlex

Background. Osteoporosis is a chronic disease that can significantly impact numerous aspects of health and wellness. The individual consequences of osteoporosis can be devastating, often resulting in substantial loss of independence and sometimes death. One of the few illnesses with greater disease burden than low bone mineral density (BMD) is major depressive disorder (MDD). Both depression and antidepressant use have been identified as secondary causes of osteoporosis. The objective of this paper is to review and summarize the current findings on the relationship between antidepressant use and BMD. Methods. Relevant sources were identified from the Pubmed and MEDLINE databases, citing articles from the first relevant publication to September 1st, 2010. Results. 2001 articles initially met the search criteria, and 35 studies were thoroughly reviewed for evidence of an association between SSRI use and BMD, and 8 clinical studies were detailed and summarized in this paper. Conclusions. Current findings suggest a link between mental illness and osteoporosis that is of clinical relevance. Additional longitudinal studies and further research on possible mechanisms surrounding the association between SSRI use on bone metabolism need to be conducted. Treatment algorithms need to recognize this association to ensure that vulnerable populations are screened.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.147
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.028
GPT teacher head0.314
Teacher spread0.286 · 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

Citations23
Published2012
Admission routes1
Has abstractyes

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