Use of Recommended Non-surgical Knee Osteoarthritis Management in Patients prior to Total Knee Arthroplasty: A Cross-sectional Study
Bibliographic record
Abstract
Objective. Our aim was to assess prior use of core recommended non-surgical treatment among patients with knee osteoarthritis (OA) scheduled for total knee arthroplasty (TKA), and to assess potential patient-level correlates of underuse, if found. Methods. This was a cross-sectional study of patients undergoing TKA for primary knee OA at 2 provincial central intake hip and knee clinics in Alberta, Canada. Standardized questionnaires assessed sociodemographic characteristics, social support, coexisting medical conditions, OA symptoms and coping, and previous non-surgical management. Multivariable logistic regression was used to assess the patient-level variables independently associated with receipt of recommended non-surgical knee OA treatment, defined as prior use of pharmacotherapy for pain, rehabilitation strategies (exercise or physiotherapy), and weight loss if overweight or obese (body mass index ≥ 25 kg/m2). Results. There were 1273 patients included: mean age 66.9 years (SD 8.7), 39.9% male, and 44.1% had less than post-secondary education. Recommended non-surgical knee OA treatment had been used by 59.7% of patients. In multivariable modeling, the odds of having received recommended non-surgical knee OA treatment were significantly and independently lower among individuals who were older (OR 0.97, 95% CI 0.95–0.99), male (OR 0.33, 0.25–0.45), and who lacked post-secondary education (OR 0.70, 0.53–0.93). Conclusion. In a large cross-sectional analysis of knee OA patients scheduled for TKA, 40% of individuals reported having not received core recommended non-surgical treatments. Older individuals, men, and those with less education had lower odds of having used recommended non-surgical OA treatments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".