Elucidating the initial symptoms and experiences of knee osteoarthritis: An international patient survey
Bibliographic record
Abstract
OBJECTIVES: Within the first phase of developing classification criteria for Early-stage Symptomatic Knee Osteoarthritis, among individuals with knee osteoarthritis (OA) we explored: 1) symptoms within the first year of noticing their knee(s); 2) features that indicated OA was the cause; and 3) timing and reasons that initially prompted seeking healthcare. DESIGN: We conducted a cross-sectional online survey of individuals with knee OA recruited from Australia, Canada, the Netherlands, and the USA. Only individuals who indicated they recalled their first symptoms were eligible. Using free-text, participants described changes in how their knee looked, felt, moved, their overall state within the first year of noticing their knee(s), features perceived to indicate knee OA was the cause, and reasons for initially seeking healthcare. We assessed the timing of seeking care by an ordinal scale. Data were analyzed using descriptive statistics and summative content analysis. RESULTS: [IQR 24,30]). Within the first year from onset, frequently reported symptoms were knee pain (70%), swelling (58%), crepitus (42%), stiffness (29%), and instability (24%). While few could provide specific clinical features, approximately half (53%) perceived medical imaging as important to indicate OA. Only 35% sought healthcare within the first year of experiencing symptoms. CONCLUSIONS: Individuals with knee OA recalled varied knee symptoms within the first year and few sought healthcare. We were unable to ascertain, from the perspective of patients, specific clinical features that indicated they had OA and not another condition.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".