Association between depression and the risk for fracture: a meta-analysis and systematic review
Bibliographic record
Abstract
BACKGROUND: Several studies have suggested that depression is associated with an increased risk for fracture; however, the results are conflicting. This study aimed to conduct a meta-analysis of cohort studies assessing the association between depression and the risk for fracture. METHODS: Relevant studies were identified by a search of Web of Science, PubMed, Embase, China National Knowledge Infrastructure and WanFang database to Feb 2018. Cohort studies on the relationship between depression and the risk for fracture in the general population were included in the meta-analysis. Data collection was in accordance with the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines, and the quality of the included studies was assessed using the Newcastle-Ottawa scale. Two independent investigators screened the abstracts and full texts of the studies, extracted data, and assessed the quality of the study. Either a fixed-effect or random-effects model was used to compute the pooled risk estimates when appropriate. RESULTS: In total, 16 cohort studies with 25 independent reports that included 414,686 participants during a follow-up duration of 3-14 years were included in the analysis. The pooled hazard ratio (HR) for total fracture was 1.24 (95% confidence interval [CI]: 1.14-1.35; P < 0.001 for heterogeneity; random-effects model). In the subgroup analyses conducted in terms of study region, the pooled HR for the studies conducted in Europe was higher (HR: 1.76; 95% CI: 1.44-2.17; P = 0.792 for heterogeneity) than that in America and Asia, with a significant difference between the groups (P = 0.036). CONCLUSION: The results of our meta-analysis suggest that depression is prospectively associated with a significantly increased risk for fracture, which may have substantial implications, both clinical and preventive.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.044 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".