Effect of Training on Patient Self‐Assessment of Joint Counts in Rheumatoid Arthritis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Patient self-assessed joint counts, if accurate and reliable, could potentially serve as a useful clinical assessment tool in rheumatoid arthritis (RA). This systematic review examines the effect of patient training on the inter-rater reliability of joint counts between patients and clinicians. METHODS: The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search was performed in PubMed, Embase, Cochrane Library, and CINAHL for articles that incorporated patient training and measured the reliability of patient self-assessed joint counts in RA. Articles were included if they reported on the inter-rater reliability between patient and clinician joint counts in both trained and untrained patients with RA. Data were extracted on characteristics of patients, structure and components of the training interventions, joint count reliability of patients with and without training, and patient feedback on training interventions. The relevant data were summarized and described. RESULTS: Multiple training methods have been studied (n = 5), including in-person sessions run by rheumatologists and instructional videos on the joint examination. Overall, training improved the reliability of patient self-joint counts, with more marked improvement in reliability of swollen joint counts than tender joint counts. Patients had positive feedback when surveyed on their experiences with training. CONCLUSION: Various training modalities (in-person and video-based) may be effective at improving reliability of patient self-joint counts. More research is needed on this topic, with potential areas for future research including 1) comparison between the efficacy of different modalities of training, and 2) impact of patient factors (education level and disease severity) on the efficacy of training.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.000 | 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".