Are Selective Serotonin Reuptake Inhibitors a Secondary Cause of Low Bone Density?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".