Self-reported painful joint count and assessor-reported tender joint count as instruments to assess pain in hand osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: To evaluate self-reported and assessor-reported joint counts for pain and their value in measuring pain and joint activity in hand OA patients. METHODS: A total of 524 patients marked painful joints on hand diagrams. Nurses assessed tenderness upon palpation. Pain was measured with a visual analogue scale pain and the Australian/Canadian hand OA index subscale pain. Synovitis and bone marrow lesions in right hand distal/proximal interphalangeal joints on MRI served as measure of joint activity. Agreement was assessed on the patient (intraclass correlation coefficient, Bland-Altman plot) and joint level (percentage absolute agreement). Correlations with measures of pain and joint activity were analysed, and joint level associations with synovitis/bone marrow lesions were calculated. RESULTS: Self-reported painful joint count (median 8, interquartile range 4-13) was consistently higher than assessor-reported tender joint count (3, 1-7). Agreement between patients and nurses on overall scores was low. Percentage absolute agreement on the joint level was 61-89%. Joint counts correlated similarly but weakly with measures of pain and joint activity (r = 0.14-0.38). On the joint level, assessor-reported tenderness was more strongly associated with synovitis/bone marrow lesions than self-reported pain. CONCLUSION: In hand OA, self- and assessor-reported joint counts cannot be used interchangeably, and measure other pain aspects than questionnaires. Assessor-reported tenderness was most closely related to MRI-defined joint activity.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".