Systematic Review and Metaanalysis of the Reproducibility of Patient Self-reported Joint Counts in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To assess the reproducibility of patient-reported tender (TJCs) and swollen joint counts (SJCs) of patients with rheumatoid arthritis (RA) compared to trained clinicians. METHODS: We conducted a systematic literature review and metaanalysis of studies comparing patient-reported TJCs and/or SJCs to clinician counts in patients with RA. We calculated pooled summary estimates for correlation. Agreement was compared using a Bland-Altman approach. RESULTS: Fourteen studies were included in the metaanalysis. There were strong correlations between clinician and patient TJCs (0.78, 95% CI 0.76-0.80), and clinician and patient SJCs (0.59, 95% CI 0.54-0.63). TJCs had good reliability, ranging from 0.51 to 0.85. SJCs had moderate reliability, ranging from 0.28 to 0.77. Agreement for TJCs reduced for higher TJC values, suggesting a positive bias for self-reported TJCs, which was not observed for SJCs. CONCLUSION: Our metaanalysis has identified a strong correlation between patient- and clinician-reported TJCs, and a moderate correlation for SJCs. Patient-reported joint counts may be suitable for use in annual review for patients in remission and in monitoring treatment response for patients with RA. However, they are likely not appropriate for decisions on commencement of biologics. Further research is needed to identify patient groups in which patient-reported joint counts are unsuitable.
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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 | 0.047 | 0.124 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.041 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".