The impact of diabetes status on pain and physical function following total joint arthroplasty for hip and knee osteoarthritis: variation by sex and body mass index
Bibliographic record
Abstract
Few studies have examined diabetes impact on total joint arthroplasty (TJA) outcomes, with variable findings. We investigated the association between diabetes and post-TJA physical function and pain, examining whether diabetes impact differs by sex and BMI. Patient sample completed questionnaires within 3 months prior to hip or knee TJA for osteoarthritis (OA) and 1-year post-surgery. Surgical 'non-response' was defined as < 30% improvement in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and physical function at 1-year. Two adjusted logistic regression models were estimated: (1) excluding, (2) including an interaction between diabetes, sex and BMI. The sample (626 hip, 754 knee) was 54.9% female, had mean BMI of 30.1, 13.0% reported diabetes. In adjusted models excluding an interaction, diabetes was not associated with non-response. However, a significant 3-way interaction (physical function: p = 0.003; pain: p = 0.006) between diabetes, sex, and BMI was found and was associated with non-response: non-response probability increased with increasing BMI in men with diabetes, but decreased with increasing BMI in women in diabetes. Findings suggest uncertainty in diabetes impact may be due to differential impacts by sex and BMI. A simple consideration of diabetes as present vs. absent may not be sufficient, with implications for the large TJA population.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".