Effect of Body Mass Index and Psychosocial Traits on Total Knee Replacement Costs in Patients with Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Clinical and psychosocial attributes are associated with clinical outcomes after total knee replacement (TKR) surgery in patients with osteoarthritis (OA), but their relationship with TKR-related costs is less clear. Our objective was to evaluate the effect of clinical and psychosocial attributes on TKR costs. METHODS: We conducted a 6-month prospective cohort study of patients with knee OA who underwent TKR. We examined baseline demographic, clinical [body mass index (BMI) and comorbidities], and psychosocial attributes (social support, locus of control, coping, depression, anxiety, stress, and self-efficacy); baseline and 6-month OA clinical outcomes [Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and function]; and 6-month direct and indirect TKR-related costs. Multiple regression was performed to identify determinants of TKR-related costs. RESULTS: We included 212 patients; 66% were women, 71% were white, and the mean age was 65.2 years. The mean baseline WOMAC pain score was 55 (SD 19) and WOMAC function score was 54 (SD 20). Mean total TKR-related costs were US$30,831 (SD $9893). Multivariate regression analyses showed that increasing BMI and anxiety levels and decreasing levels of positive social interactions were associated with increased costs. A lower cost scenario with a lower range of normal BMI (19.5), highest positive social interaction, and no anxiety predicted TKR costs to be $22,247. Predicted costs in obese patients (BMI 36) with lowest positive social interaction and highest anxiety were $58,447. CONCLUSION: Increased baseline BMI, anxiety, and poor social support lead to higher TKR-related costs in patients with knee OA. Preoperative interventions targeting these factors may reduce TKR-related costs, and therefore be cost-effective.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".