OARSI initiative to develop classification criteria for early-stage symptomatic knee OA (EsSKOA): What conditions should be considered in the differential diagnosis of EsSKOA?
Bibliographic record
Abstract
OBJECTIVE: Classification criteria for early-stage symptomatic knee osteoarthritis (EsSKOA) should discriminate individuals with EsSKOA from those with other causes of knee symptoms. We sought to identify conditions in the differential diagnosis of EsSKOA in adults with knee symptoms. DESIGN: We conducted an online survey of clinicians. Those consulting monthly on at least five people with undiagnosed knee symptoms were eligible. From qualitative work and clinical experience, we developed three case scenarios representing possible EsSKOA: 1. 40-year-old with 1 month of knee stiffness and swelling; 2. 50-year-old with 8 months of knee discomfort while walking; and 3. 60-year-old with intense knee discomfort getting out of a car 1 week ago. For each scenario, participants indicated conditions on a pre-defined list that they would consider in the differential diagnosis, and the top three diagnoses based on clinical experience. The proportions that considered each condition and among the top three diagnoses for each scenario were summarized overall and by clinical discipline. RESULTS: 127 clinicians responded (43% female, 48% in practice ≤15 years, 50% university-affiliated practice, 7 clinical disciplines). Knee OA and meniscal injuries were among the top three conditions in the differential diagnosis for all three scenarios, followed by immune-mediated and crystal-induced inflammatory arthritis (scenario 1), patellofemoral pain syndrome (scenario 2), and collateral ligament injuries (scenario 3). CONCLUSION: The differential diagnosis for EsSKOA in adults presenting with undiagnosed knee symptoms includes symptomatic established radiographic knee OA, patellofemoral pain syndrome, meniscal and collateral ligament injuries, and immune-mediated and crystal-induced inflammatory arthritis.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".