Characterizing Pain Flares From the Perspective of Individuals With Symptomatic Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Although pain in knee osteoarthritis (OA) commonly affects activity engagement, the daily pain experience has not been fully characterized. Specifically, the nature and impact of pain flares is not well understood. This study characterized pain flares as defined by participants with knee OA. Pain flare occurrence and experience were measured over 7 days. METHODS: This was a multiple methods study; qualitative methods were dominant. Data were collected during the baseline portion of a randomized controlled trial. Participants met criteria for knee OA and had moderate to severe pain. They completed questionnaires and a 7-day home monitoring period that captured momentary symptom reports simultaneously with physical activity via accelerometry (n = 45). Participants also provided individual definitions of pain flare that were used throughout the home monitoring period to indicate whether a pain flare occurred. RESULTS: Pain flares were described most often by quality (often sharp), followed by timing (seconds, minutes) and by antecedents and consequences. When asked if their definition of a flare agreed with a supplied definition, 49% of the sample reported only "somewhat," "a little," or "not at all." Using individual definitions, 78% experienced at least 1 daily pain flare over the home monitoring period; 24% had a flare on more than 50% of the monitored days. CONCLUSION: Pain flares were common, fleeting, and often experienced in the context of activity engagement. Participants' views on what constitutes a pain flare differ from commonly accepted definitions. Pain flares are an understudied aspect of the knee OA pain experience and require further characterization.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".