Surgical Healing Beyond the Scalpel: Exploring the Impact of Depressive Symptoms on Functional Recovery in Total Knee Arthroplasty Patients
Bibliographic record
Abstract
Abstract Introduction: Numerous recent studies have explored the association between the mental health condition of patients before surgery and the outcomes of total knee arthroplasty. The principal objective of this study was to determine the prevalence of depressive symptoms among individuals undergoing total knee arthroplasty. Additionally, the study aimed to investigate the impact of pre-operative depressive symptoms as a significant and independent predictor on various health-related quality of life measures for patients undergoing knee surgery. Material and Methods: During the period spanning from August 2019 to May 2020, an orthopaedic database was established for the purpose of assessing patients' conditions before their surgeries. The data collection process occurred at three distinct intervals: prior to the surgery, as well as at the third and sixth months following the surgical procedure. In this study, we undertook an evaluation of both preoperative and postoperative depressive symptoms, as well as functional status, utilizing various self-report measures. These measures included the Becks Depression Scale, the Western Ontario and McMaster Universities Osteoarthritis Index, and the Knee Society Clinical Rating System. Results: Over the course of the study, the proportion of distressed patients decreased from 99% at the baseline assessment to 85% classified as normal, with 15% displaying mild distress levels during the 6-month follow-up period. Conclusions: Patients experiencing depression exhibited notable enhancements in various outcome measures. The findings from this study underscore a two-way relationship between mental health and surgical outcomes. Specifically, the surgical intervention yielded significant improvements in mental health status. Conversely, poorer preoperative mental health status emerged as a predictive factor for comparatively less favorable outcomes stemming from the surgery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".