The impact of diabetes on physical and mental health status and patient satisfaction after total hip and knee arthroplasty
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of diabetes on physical and mental health status, as well as patient satisfaction, one-year following knee and hip total joint arthroplasty (TJA) for osteoarthritis (OA). METHODS: Participants were 626 hip and 754 knee TJA patients. Pre-surgery data were collected on socio-demographics and health status. The 12-item Short Form Health Survey (SF-12) was collected pre- and one year post-surgery, and physical (PCS) and mental component (MCS) summary scores computed. One-year patient satisfaction was also recorded. Four regression models tested the effect of diabetes on: 1) PCS change score; 2) MCS change score; 3) achieving minimal clinically important improvement (MCII) on PCS; and 4) patient satisfaction ('Somewhat or Very Satisfied' vs. 'Somewhat or Very Dissatisfied'). An interaction between surgical joint and diabetes was tested in each model. RESULTS: Self-reported diabetes prevalence was 13.0% (95% CI: 11.2%-14.7%) and was more common in knee 16.1% (95% CI: 13.4%-18.7%) than hip 9.3% (95% CI: 7.0%-11.5%) patients. In adjusted analyses, change scores were 2.3 units less on the PCS for those with diabetes compared to those without (p = 0.005). Patients with diabetes were about half as likely to achieve MCII as patients without diabetes (p = 0.004). Diabetes was not significantly associated with satisfaction or changes in MCS scores. Diabetes effects did not differ by surgical joint. CONCLUSIONS: Findings support that diabetes has a negative impact on improvements in physical health after TJA. Considering the growing prevalence of OA and diabetes in the population, our findings support the importance of perioperative screening and management of diabetes in patients undergoing TJA.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".