The rate of unnecessary interventions for the management of knee osteoarthritis: a population-based cohort study
Bibliographic record
Abstract
Background: Several commonly used procedures for knee osteoarthritis (OA) are not supported by evidence-based guidelines. The objective of this study was to identify the proportion of patients who underwent knee arthroscopy or magnetic resonance imaging (MRI) and the timing of these procedures before total knee arthroplasty (TKA). Methods: We conducted a retrospective cohort study using administrative data sets from Ontario, Canada. We identified the proportion of patients who underwent knee arthroscopy in the previous 10 years or an MRI in the 3 years before their primary TKA. We also evaluated the rate of arthroscopies by diagnosis. We report the timing of each outcome in relation to the TKA, rates by geographical area, and differences in rates over time. Results: We included 142 275 patients, of whom 36 379 (25.57%) underwent knee arthroscopy (median time 2.8 [interquartile range (IQR) 1.1–6.0] years); 22% of those were within 1 year of TKA and 52% were within 3 years. The rates of arthroscopies for a diagnosis of osteoarthritis (OA) steadily decreased, while those for meniscal-related diagnoses increased over the study period (p < 0.0001). There was significant variation by region. Of the cohort, 23.2% (n = 32 989) had an MRI before their TKA, with rates significantly increasing over time (p < 0.0001). Conclusion: A substantial proportion of patients with knee OA received diagnostic and therapeutic interventions before TKA that are contrary to clinical practice 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".