IMPACT OF INTERVENTIONS ON DEPRESSION SCORES AND OUTCOMES IN PATIENTS UNDERGOING TOTAL JOINT REPLACEMENT: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Depression is a significant contributor to suboptimal outcomes following total joint arthroplasty (TJA). Patients undergoing TJA with unrecognized or undiagnosed depression are at risk for poorer outcomes. In the appropriate context, such patients may benefit from interventions which improve depression outcomes. As such, we conducted a systematic review of comparative studies to review the available evidence of interventions that impacted depression scores and/or outcomes for patients undergoing TJA. Embase, Ovid Medline, PubMed, and Scopus were reviewed systematically from inception until August 8, 2021. Studies that were relevant for this review included comparative studies between patients that received an intervention prior to or following their primary TJA procedure and a control group that reported on depression outcomes/scores following surgery. The final systematic review consisted of 25 relevant studies. Interventions which improved depression scores during at least one follow up time after TJA included duloxetine, zolpidem, cognitive behavioural therapy (CBT), psychological support, Videoinsight enhancing psychological method, supervised physiotherapy, telephone follow-up, nursing as well as sensory and coping information. In patients that are at risk for undergoing TJA with undiagnosed depression, surgeons should consider incorporating and emphasizing treatment interventions in their peri-operative management and treatment, when applicable, to improve outcomes. The current study directs future research endeavours, highlighting interventions in which further investigation is warranted to investigate their potential role for improving outcomes in patients with diagnosed depression undergoing TJA.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".