Importance of self‐rated health and mental well‐being in predicting health outcomes following total joint replacement surgery for osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: The determinants of outcomes and the scope of outcomes examined in total joint replacement (TJR) typically have been limited to aspects of physical health. We investigated mental well-being, physical and social health, and self-rated health (SRH) as predictors of future health status within a cohort undergoing a TJR for hip or knee osteoarthritis. We also investigated the interrelationships among these health dimensions as they relate to SRH. METHODS: Participants (n = 215 hip, n = 234 knee) completed measures presurgery and 3 and 6 months postsurgery, including pain, physical function, fatigue, anxiety, depression, social participation limitations, passive/active recreation, community mobility, and SRH. Structural equation modeling was used to investigate the interrelationship between 3 latent health dimensions (physical, mental, social) and the predictive significance of SRH for future health status. RESULTS: The mean age was 63.5 years (range 31-88 years) and 60% were women. Prior dimension status strongly predicted future status. Adjusted for prior dimension scores, comorbidity, and sociodemographic characteristics, SRH predicted future scores for all 3 health dimensions. Worse prior SRH predicted less improvement at all time points. The effects of physical and social health on SRH were fully mediated through mental well-being. Only mental well-being significantly predicted SRH, within and across time. CONCLUSION: Mental well-being is critical for understanding the relationship between physical health and SRH. In addition, SRH significantly predicts TJR outcomes, above and beyond prior physical health. The exclusive focus on any one health dimension may lead to missed opportunities for predicting and improving outcomes following surgery, and likely improving overall health generally.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".