The impact of depression and anxiety disorders on postoperative outcomes for patients having total hip or knee arthroplasty: Protocol of a meta-analytic study from cohort studies
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are widely performed surgeries for end-stage joint disease, yet the influence of depression and anxiety on postoperative outcomes remains unclear. This study aims to consolidate current evidence on the relationship between preoperative depression and/or anxiety disorders and postoperative outcomes in adult patients undergoing primary THA or TKA. Given the potential for these psychiatric conditions to affect recovery, pain management, and overall satisfaction, the results of this study are crucial to inform targeted perioperative interventions and improve patient-centered care. METHODS AND ANALYSIS: We will search PubMed, Embase, Cochrane Library and PsycINFO from inception to the November 2024, adopting a comprehensive search strategy with no language restrictions. Eligible studies will include cohort studies evaluating adults with a diagnosis of depression and/or anxiety before THA or TKA compared to those without such disorders. Inclusion criteria will focus on preoperative psychiatric diagnoses, clearly defined postoperative outcomes (such as complications, functional recovery measures, pain, length of stay, and patient-reported outcomes). Risk of bias assessment will be performed using the Newcastle-Ottawa Scale. Meta-analysis will be conducted using a random-effects model to calculate pooled risk estimates and 95% confidence intervals for each outcome. Heterogeneity will be quantified with the I2 statistic, and a threshold of I2 > 50% will indicate substantial heterogeneity. Sources of heterogeneity will be explored via subgroup analyses or meta-regression if possible. Potential publication bias will be visually assessed using funnel plots and statistically tested using Egger's test. Sensitivity analyses will be carried out to evaluate the robustness of the results for each outcome through leave-one-out procedure. DISCUSSION: This study will introduce a systematic and rigorous approach to synthesizing evidence from multiple cohorts, providing a comprehensive understanding of the impact of depression and anxiety on THA and TKA outcomes. The findings will guide clinicians in recognizing and managing mental health issues to optimize postoperative recovery and ultimately improve patient satisfaction and quality of life. STUDY REGISTRATION: Study registration: CRD42024500008.
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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 | 0.076 | 0.113 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.055 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".