Sex‐Modified Effects of Depression, Low Back Pain, and Comorbidities on Pain After Total Knee Arthroplasty for Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The influence of sex on post-total knee arthroplasty (TKA) outcomes has been variable in the literature. Though sex is often reported as an averaged effect, we undertook this study to investigate whether sex modified the influence of presurgery characteristics on post-TKA knee pain. METHODS: This was a prospective study with data derived from 477 TKA osteoarthritis patients (279 women, 198 men). Questionnaires were completed presurgery and at 3 months postsurgery. The association between 3-month post-TKA knee pain and presurgery covariates (body mass index, comorbidity count, symptomatic joint count, low back pain, knee pain, and depressive symptoms) was assessed by linear regression. Sex-specific effects were evaluated using interactions. RESULTS: Women had significantly worse presurgery knee pain, joint count, and depressive symptoms, and worse postsurgery knee pain, than men. With simple covariate adjustment, no sex effect on pain was found. However, sex was found to moderate the effects of comorbidities (worse for women [P = 0.013]), presence of low back pain (worse for men [P = 0.003]), and depressive symptoms (worse for men [P < 0.001]) on postsurgery pain. Worse presurgery pain was associated with worse postsurgery pain similarly for women and men. CONCLUSION: The influence of some patient factors on early post-TKA pain cannot be assumed to be the same for women and men; average effects may mask underlying associations. Results suggest a need to consider sex differences in understanding TKA outcomes, which may have important implications for prognostic tool development in TKA.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".