Measuring Pain and Efficacy of Pain Treatment in Inflammatory Arthritis: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: To systematically review the available literature on measuring pain and the efficacy of pain treatment in inflammatory arthritis (IA), as an evidence base for generating clinical practice recommendations. METHODS: A systematic literature search was performed in Medline, Embase, Cochrane Library, and the American College of Rheumatology/European League Against Rheumatism 2008/2009 meeting abstracts, searching for studies evaluating clinimetric properties of pain measurement tools in IA (convergent validity, internal consistency, retest reliability, responsiveness, feasibility, and standardization). Studies that presented information on these properties were reviewed and their data were integrated into the pool of results available for pain measures in IA. RESULTS: In total, 51 articles were included in the review. Validated information on pain was available for tools covering different facets such as overall pain, anatomically specific pain, or a mixture of both. Data from these studies showed that single pain-related items such as the visual analog scale (VAS), numeric rating scale (NRS), or verbal rating scale (VRS) provide sufficient clinimetric information. Similar results were obtained for the pain subscales of the Arthritis Impact Measurement Scales (AIMS/AIMS2) and the bodily pain subscale of the Medical Outcome Study Short-Form Survey 36. Most clinimetric coefficients showed acceptable results with respect to validity, reliability, and sensitivity to change, while the degree of standardization and feasibility mostly filled at least 2 of 3 predefined criteria. CONCLUSION: A variety of pain measures are available to cover different aspects of pain such as intensity, frequency, or location. Single-item tools such as VAS, NRS, or VRS can be recommended to measure overall pain in clinical practice. If more specific issues need to be addressed, more sophisticated tools should be taken into account.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".