Physician global assessments for disease activity in rheumatoid arthritis are all over the map!
Bibliographic record
Abstract
INTRODUCTION: Physician global assessments of disease activity (medical doctor (MD) globals) are important outcomes. MD globals may vary based on their age, gender, practice setting and experience (number of patients seen per year and years in practice). METHODS: We determined the variability of MD globals, surveying rheumatologists from the Canadian Rheumatology Association using rheumatoid arthiritis (RA) cases rated by MD for disease activity from 0 to 10. Cases were developed to span the spectrum of disease activity. Kappa, intraclass correlation (ICC) coefficients and linear mixed models were used. RESULTS: 109 responded to the survey (approximately 30% response). The range of MD globals for the same scenario was as high as 7.6 out of 10, indicating vast discrepancies between physicians. Some scenarios outlined changes in individual patients; however, physicians surveyed were often in disagreement as to how much the patient recovered or worsened but the direction was the same (ie, if better all agreed). When physician-related factors were analysed separately, MD global scores were significantly influenced by age and experience (ranked by a physician, number of patients seen per year and years in clinical practice) in linear mixed models. Multivariate analysis revealed borderline significance for two age categories (56-65 years, P=0.049; over 65 years, P=0.058) and those who have seen 600-800 patients per year (P=0.056). CONCLUSIONS: This emphasises the need to establish evaluation criteria in RA for disease. Perhaps, a catalogue of patient scenarios that range from 0 to 10 could be developed, standardised and agreed on to decrease the wide variability of ranking by rheumatologists.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".