“You don't put it down to arthritis”: A qualitative study of the first symptoms recalled by individuals with knee osteoarthritis
Bibliographic record
Abstract
Objective: As part of the first phase of the OARSI Early-stage Symptomatic Knee Osteoarthritis (EsSKOA) initiative, we explored the first symptoms and experiences recalled by individuals with knee osteoarthritis (OA). Design: This qualitative study, informed by qualitative description, was a secondary analysis of focus groups (n = 17 groups) and one-on-one interviews (n = 3) conducted in 91 individuals living with knee OA as part of an international study to better understand the OA pain experience. In each focus group or interview, participants were asked to describe their first symptoms of knee OA. We inductively coded these transcripts and conducted thematic analysis. Results: . Participants described the gradual and intermittent way in which symptoms of knee OA developed over many years; many could not identify a specific starting point. Participants described diverse initial knee symptoms, including activity-exacerbated joint pain, stiffness and crepitus. Most participants dismissed early symptoms or rationalized their presence, employing various strategies to enable continued participation in recreational and daily activities. Few sought medical attention until physical functioning was demonstrably impacted. Conclusions: The earliest symptoms of knee OA are frequently insidious in onset, episodic and present long before individuals present to health professionals. These results highlight challenges to identifying people with knee OA early and support the development of specific classification criteria for EsSKOA to capture individuals at an early stage.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".