Selective serotonin reuptake inhibitor and selective serotonin and norepinephrine reuptake inhibitor use and risk of fractures in adults: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between selective serotonin reuptake inhibitor (SSRI) and selective serotonin and norepinephrine reuptake inhibitor (SNRI) use and risk of fractures in older adults. METHODS: We systematically identified and analyzed observational studies comparing SSRI/SNRI use for depression with non-SSRI/SNRI use with a primary outcome of risk of fractures in older adults. We searched for studies in MEDLINE, PsycINFO, Embase, DARE (Database of Abstracts or Reviews of Effects), the Cochrane Library, and Web of Science clinical trial research registers from 2011 for SSRIs and 1990 for SNRIs to November 29, 2016. RESULTS: Thirty-three studies met our inclusion criteria; 23 studies were included in meta-analysis: 9 case-control studies and 14 cohort studies. A 1.67-fold increase in the risk of fracture for SSRI users compared with nonusers was observed (relative risk 1.67, 95% CI 1.56-1.79, P = .000). The risk of fracture increases with their long-term use: within 1 year, the risk is 2.9% or 1 additional fracture in every 85 users; within 5 years, the risk is 13.4% or 1 additional fracture in every 19 users. In meta-regression, we found that the increase in risk did not differ across age groups (odds ratio = 1.006; P = .173). A limited number of studies on SNRI use and the risk of fractures prevented us from conducting a meta-analysis. CONCLUSIONS: Our systematic review showed an association between risk of fracture and the use of SSRIs, especially with increasing use. Age does not increase this risk. No such conclusions can be drawn about the effect of SNRIs on the risk of fracture because of a lack of studies.
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 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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".