Patient-reported Joint Count in Juvenile Idiopathic Arthritis: The Reliability of a Manikin Format
Bibliographic record
Abstract
OBJECTIVE: To evaluate the reliability of a manikin format, patient-reported joint count in juvenile idiopathic arthritis (JIA), and to detect changes in agreement at a second visit. METHODS: Patients with JIA aged 12-21 were asked to mark joints with active arthritis on a manikin before their regular clinic visit. The physician then performed a joint count without having seen the patient's assessment. Agreement between scores of physician-reported and patient-reported joint counts was assessed using ICC. Kappa statistics were used to assess reliability of scoring individual joints. RESULTS: The study included 75 patients with JIA. In general, patients had a low number of active joints (median 1 joint, indicated by the physician). ICC was moderate (0.61) and κ ranged from 0.3-0.7. At the second visit, κ were similar; the ICC was 0.19. When a patient scored 0 joints, the physician confirmed this 93%-100% of the time. When the patient marked ≥ 1 joints, the physician confirmed arthritis 59%-76% of the time. Sensitivity to change was moderate. CONCLUSION: Agreement between physician and patient on the number of joints with active arthritis was reasonable. Untrained patients tended to overestimate the presence of arthritis when they marked active joints on a manikin-format joint count. When the patient indicated absence of arthritis, the physician usually confirmed this. As the agreement did not improve at followup, future research should focus on the possibility of achieving this through training. For now, the patient-reported joint count cannot replace the physicians' joint count in clinical practice; it may be used in epidemiological studies with caution.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".