Treatment of Concurrent Depression and Its Effect on Outcomes After Total Joint Arthroplasty: A Systematic Review of Comparative Studies
Bibliographic record
Abstract
PURPOSE: There is a growing body of evidence suggesting that patients with a diagnosis of depression suffer worse outcomes after total joint arthroplasty (TJA) procedures. It is unclear whether depression treatment is a modifiable risk factor that can be targeted to improve suboptimal outcomes. We conducted a systematic review to understand the role that various interventions have on outcomes of TJA in patients with diagnosed depression (PDDs). METHODS: PubMed, Ovid MEDLINE, Scopus, and EMBASE were searched systematically from inception until November 2022. Studies of PDDs who underwent TJA that compared any intervention/treatment of depression with a control group and reported pain, functional outcomes, depression scores, and/or revision rates after TJA were relevant for this review. RESULTS: Ten relevant studies were included in the final systematic review, with a total of 33,501 patients included. Two studies reported lower revision rates for patients receiving selective serotonin reuptake inhibitor treatment and psychotherapy. Two studies showed no difference in functional outcomes for patients receiving pharmacologic treatment. One study reported improved functional outcomes for patients receiving cognitive behavioral therapy and another reported improved postoperative functional outcomes for patients receiving alprazolam. CONCLUSION: Interventions targeted at PDDs may improve short-term pain and functional outcomes, and there may be an association between selective serotonin reuptake inhibitor use and implant survival. The current literature is limited and inconclusive, with important gaps in understanding regarding the optimization and treatment of this modifiable risk factor. Surgeons should consider depression treatment as a method to improve outcomes in this cohort.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".