Use of the 28-Joint Count Yields Significantly Higher Concordance Between Different Examiners Than the 66/68-Joint Count
Bibliographic record
Abstract
OBJECTIVE: Joint counts are the key outcome measure in rheumatoid arthritis (RA). There is a great variability between different assessors of the same patient; this variability can be reduced by standardized training. The training effect is far less pronounced for the 66/68-joint count compared to the 28-joint count. We evaluated the reason for the higher interrater disagreement in the 66/68 compared to the 28-joint count. METHODS: Participants in joint examination seminars evaluated a patient with RA before and after training in the European League Against Rheumatism technique. Joints were rated positive or negative for tenderness and swelling. The number of positive joints and the variability between examiners before and after the training were compared. Concordance was calculated for every single joint using the Fleiss-Kappa test. RESULTS: In total, 256 health professionals were instructed in the 66/68-joint count and 84 in the 28-joint count. The disagreement between examiners was higher for swelling than for tenderness. After the training, there was a significant reduction of interrater variability, which was more pronounced in the 28 than in the 66/68-joint count. Comparisons between joint counts revealed that the joints of the feet were more likely to be rated negative, yet interrater disagreement was still high. CONCLUSION: Standardization of joint examination significantly reduces variability between assessors. The better performance of the 28-joint count is due to the lower number of joints examined, especially the foot joints, which remain difficult to assess reliably even after training.
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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 | 0.033 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".