The Influence of Major Depressive Disorder at Both the Preoperative and Postoperative Evaluations for Total Knee Arthroplasty Outcomes
Bibliographic record
Abstract
OBJECTIVE: The purpose of this paper is to analyze the impact of major depressive disorder, both preoperatively and one year postoperatively, on the functional and psychosocial outcomes of total knee arthroplasty (TKA). METHODS: Two hundred sixty patients undergoing a total knee arthroplasty completed both the baseline and 12-month follow-up assessments. Short-Form Health Inventory (SF36), Western Ontario and McMaster University Arthritis Index (WOMAC), and Knee Society Score (KSS) were measured both preoperatively and postoperatively. The Patient Health Questionnaire (PHQ) was used to diagnose major depressive disorder (MDD) at baseline and follow-up; patients were then classified into one of four groups: No MDD, Lost MDD, Gained MDD, and Continuous MDD. Univariate analysis compared the four groups at baseline, one-year follow-up, and change scores using a Kruskal-Wallis test for continuous data or a chi-square test of independence for categorical data. RESULTS: Two hundred seven (79.60%) patients were in the No MDD group, 22 (8.50%) patients were in the Lost MDD group, 19 (7.30%) patients were in the Gained MDD group, and 12 (4.60%) patients were in the Continuous MDD group. There were significant between-group differences present in baseline measures of WOMAC and SF36 mental health summary. In addition, there were significant group differences in the follow-up WOMAC, KSS, and SF36 scores. CONCLUSIONS: Depression was associated with poorer preoperative and postoperative TKA scores. Patients who were depressed 12 months after surgery demonstrated poorer recovery than patients who did not show depressive symptoms before TKA or within the year after.
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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 | 0.001 | 0.004 |
| 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".