Standardization of Joint Examination Technique Leads to a Significant Decrease in Variability Among Different Examiners
Bibliographic record
Abstract
OBJECTIVE: To reduce the amount of variability among assessors, we conducted joint examination standardization seminars in conjunction with multicenter clinical trials for patients with rheumatoid arthritis (RA). The examination techniques used were based on the recommendations of the European League Against Rheumatism (EULAR). METHODS: To evaluate the effect of standardization, participants at the seminars examined a given patient with RA before and after they were made familiar with the EULAR examination technique. The number of tender and swollen joints as well as the variance among the examiners before and after the training were compared. Joints were rated positive or negative for tenderness and swelling without grading. RESULTS: Overall, 553 individuals from a variety of countries in Europe, North America, Asia, and Australia participated. Examiners included different kinds of health professionals, mainly physicians and nurses. We found a substantial variance among examiners before the training in the standardized method. This variance could be significantly reduced by the training. We also found that the number of joints considered active was markedly reduced after the training. CONCLUSION: Standardized joint examination training significantly reduces variability among different assessors.
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".