Influence of depression on functional outcomes in patients with knee osteoarthritis undergone unicompartmental knee arthroplasty or total knee arthroplasty: A prospective study
Bibliographic record
Abstract
BACKGROUND: Total knee arthroplasty (TKA) and unilateral knee arthroplasty (UKA) are often the treatment of choice for knee osteoarthritis. Approximately 20% of patients affected by osteoarthritis suffer from depressive symptoms. OBJECTIVE: The present study aims to evaluate the influence of depression on functional outcomes in patients with knee osteoarthritis undergone UKA and TKA. METHODS: Depression was assessed using the preoperative Geriatric Depression Scale (GDS), on postoperative outcomes of TKA and UKA measured using Forgotten Joint Score-12 (FJS-12), Short Form Health Survey-36 (SF-36), Western Ontario and McMaster Universities Arthritis Index (WOMAC), Oxford Knee Score (OKS), and Barthel Index. RESULTS: = 0.046) at 6-month follow-up. Patients that underwent UKA with a higher GDS score preoperatively were found to have a higher WOMAC functional limitations score postoperatively. Other statistically significant correlations between preoperative GDA and postoperative outcome scores following UKA and TKA were not found. CONCLUSION: Taken together, findings of our study suggested that more literature is needed to fully elucidate the influence of psychological factors such as depression and depressive symptoms on postoperative outcomes of UKA and TKA. Understanding such correlations is potentially beneficial in the development of preoperative and postoperative treatment programs that deal with psychosocial components of illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".