Experiencing painful osteoarthritis: what have we learned from listening?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite the central role of pain in osteoarthritis, until recently, relatively little attention was paid to the osteoarthritis pain experience, including the features of osteoarthritis pain that are most important to people living with this disease. The focus of this review is on recent advances in our understanding of the experience of osteoarthritis pain from the patient's perspective. RECENT FINDINGS: To gain an understanding of the experience of pain in osteoarthritis, researchers have largely relied on qualitative methodologies. This research indicates that the osteoarthritis pain experience is multidimensional, reflecting the influence of biological (e.g. pain mechanisms), psychological (e.g. mood and coping), and social factors (e.g. social support). Qualitative and quantitative research to date supports the need for measures that distinguish aspects of the pain itself (intensity, frequency, quality, location, etc.) from the consequences of the pain on activity limitations and participation restriction, mood, sleep, and health-related quality of life. This research has underscored the limitations of existing generic and osteoarthritis-specific pain measures, and is driving the development of new tools to better evaluate osteoarthritis-related pain, and thus assessment of its impact and response to various interventions. SUMMARY: Improved measurement of painful osteoarthritis, including attention to the words people with osteoarthritis use to describe their pain, will undoubtedly lead to an improved understanding of pain mechanisms in osteoarthritis and, in turn, mechanism-based and evidence-based treatment decision making.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".