Prevalence of Depression and Posttraumatic Stress Disorder After Acute Orthopaedic Trauma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: This study aims to systematically assess the existing literature and to derive a pooled estimate of the prevalence of depression and posttraumatic stress disorder (PTSD) in adult patients after acute orthopaedic trauma. DATA SOURCES: A comprehensive search of databases, including MEDLINE, Embase, PsycINFO, and Cochrane Central Register of Controlled Trials databases was conducted through June 2015. STUDY SELECTION: We included studies that assessed the prevalence of depression or PTSD in patients who experienced acute orthopaedic trauma to the appendicular skeleton or pelvis. Studies with a sample size of ≤10 were excluded. DATA EXTRACTION: Two authors independently extracted data from the selected studies and the data collected were compared with verify agreement. DATA SYNTHESIS: Twenty-seven studies and 7109 subjects were included in the analysis. Using a random-effects model, the weighted pooled prevalence of depression was 32.6% (95% CI, 25.0%-41.2%) and the weighted pooled prevalence of PTSD was 26.6% (95% CI, 19.0%-35.9%). Six studies evaluated the prevalence of both depression and PTSD in patients with acute orthopaedic injuries. The weighted pooled prevalence of both depression and PTSD for those patients was 16.8% (95% CI, 9.0%-29.4%). CONCLUSIONS: Nearly one-third of patients suffer from depression and more than one-quarter of patients suffer from PTSD after an acute orthopaedic injury suggesting that strategies to address both the mental and physical rehabilitation after an orthopaedic injury should be considered to optimize patient recovery. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.026 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".