Do people with knee osteoarthritis use guideline-consistent treatments after an orthopaedic surgeon recommends nonsurgical care? A cross-sectional survey with long-term follow-up
Bibliographic record
Abstract
Objective: Describe "usual care" patterns of education, exercise, weight management, pain medication and other nonsurgical treatments for knee osteoarthritis (OA) in people recommended for nonsurgical care by an orthopaedic surgeon. Methods: We used a telephone-administered questionnaire to capture treatments people with knee OA used over the three to six years after an orthopaedic surgeon recommended nonsurgical care. The primary outcome, guideline-consistent nonsurgical treatments, was an aggregate measure defined as using education, exercise, weight management, and at least one recommended medication. Secondary outcomes were first-line (education, exercise, and weight management) and guideline-inconsistent treatments (orthoses, opioids, hyaluronic acid, platelet rich plasma, and stem cell therapy). Multivariable robust Poisson regression assessed the association between participant characteristics and use of guideline-consistent, first-line and guideline-inconsistent treatments. Results: 479 people were invited and 250 participated (52%). Participants were 58% female with a mean age 66.2 years. Participants received education by a healthcare professional (64%), exercised regularly (74%), used weight management (38%), and used recommended pain medications (91%). All guideline-consistent nonsurgical treatments were used by 19% of participants, 19% of participants used first-line treatments, and 42% used guideline-inconsistent treatments. Over six years, 34% had another consult then underwent arthroplasty. Older participants were less likely to use any treatment. People without post-secondary education were less likely to use first-line treatments (RR 0.54, 95% CI: 0.30-0.96), and females were less likely to use guideline-inconsistent treatments (RR 0.62, 95% CI:0.47-0.81). Conclusions: Nonsurgical usual care for people with knee OA was not consistent with international clinical guidelines.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".