Patient Experience of Stiffness With Knee Osteoarthritis: An Interpretative Description Study
Bibliographic record
Abstract
INTRODUCTION: Knee OA (KOA) can lead to pain, loss of muscle strength, and changes in gait. Knee stiffness is a classic feature of KOA that can increase the risk of falls but has been understudied. OBJECTIVE: To evaluate the impact of knee stiffness, the factors influencing the severity of stiffness, and the repercussions on participation for patients with KOA. METHODS: This qualitative study used an interpretive description approach. Purposeful sampling was used for patients with KOA over 45 years of age, fluent in English, diagnosed with KOA and reported KOA stiffness within the last 6 months. Participants were recruited through social media and Ontario clinics. Semi-structured interviews were conducted over the phone or using zoom, recorded, and transcribed verbatim. Open, axial, and selective coding were used to identify clinically relevant themes. RESULTS: Twelve participants (5F, 7M) with a mean age of 60 years were included. The five themes identified include elusive and variable perceptions of joint stiffness, inactivity or too much activity exacerbates stiffness, adapting to the ebb and flow of symptoms, risk experiences and safety fears leads to reduced participation, and KOA stiffness impairs quality of life. CONCLUSION: This study highlights characteristics of knee stiffness, consequences on participation, and quality of life for people with KOA. Monitoring knee stiffness for KOA is recommended for more appropriate treatment intensity, which could improve adherence to a home programme and potentially reduce the risk of falls.
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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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".