Systematic Review and Metaanalysis of Patient Self-Report versus Trained Assessor Joint Counts in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Patient self-report outcomes and physician-performed joint counts are important measures of disease activity and treatment response. This metaanalysis examines the degree of concordance in joint counts between trained assessors and patients with rheumatoid arthritis (RA). METHODS: Studies eligible for inclusion met the following criteria: English language; compared patient with trained assessor joint counts; peer-reviewed; and RA diagnosis determined by board-certified or board-eligible specialist or met 1987 American College of Rheumatology criteria. We searched PubMed and Embase to identify articles between 1966 and January 1, 2008. We compared measures of correlation between patients and assessors for either tender/painful or swollen joint counts. We used metaanalysis methods to calculate summary correlation estimates. RESULTS: We retrieved 462 articles and 18 were included. Self-report joint counts were obtained by a text and/or mannequin (picture) format. The summary estimates for the Pearson correlation coefficients for tender joint counts were 0.61 (0.47 lower, 0.75 upper) and for swollen joint counts 0.44 (0.15, 0.73). Summary results for the Spearman correlation coefficients were 0.60 (0.30, 0.90) for tender joint counts and 0.54 (0.35, 0.73) for swollen joint counts. CONCLUSION: A self-report tender joint count has moderate to marked correlation with those performed by a trained assessor. In contrast, swollen joint counts demonstrate lower levels of correlation. Future research should explore whether integrating self-report tender joint counts into routine care can improve efficiency and quality of care, while directly involving patients in assessment of RA disease activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.077 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.003 | 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".