Effect of Rheumatologist Education on Systematic Measurements and Treatment Decisions in Rheumatoid Arthritis: The Metrix Study
Bibliographic record
Abstract
OBJECTIVE: To determine whether an educational intervention could result in changes in physicians' practice behavior. METHODS: Twenty rheumatologists performed a prospective chart audit of 50 consecutive patients with rheumatoid arthritis (RA) and again after 6 months. Ten were randomized to the educational intervention: monthly Web-based conferences on the value of systematic assessments in RA, recent evidence-based information, practice efficiency, and other topics; this group also read articles on targeting care in RA. The others were randomized to no intervention. RESULTS: One thousand serial RA charts were audited at baseline and 1000 at 6 months, with no between-group differences in patient characteristics: mean disease duration of 10 years; 77% women; 74% rheumatoid factor- positive; mean Disease Activity Score (DAS) 3.7; and 68% taking methotrexate, 14% taking steroids, and 27% taking biologics. At 6 months the intervention group collected more global assessments (patient global 53% preintervention vs 66% postintervention, and MD global 51% vs 60%; p < 0.05) and Health Assessment Questionnaires (37% vs 42%; p > 0.05; p = nonsignificant), whereas controls had no change in outcomes collected. For the intervention group there was a 32% increase in calculable composite scores [such as DAS, Simplified Disease Activity Index (SDAI), Clinical Disease Activity Index; p < 0.05] but no change in the controls. There was more targeting to a low disease state. For those with SDAI between 3.3 and 11, the percentage of patients receiving a change in therapy was 66% in the intervention group and 36% in controls (p < 0.05). When DAS was between 2.4 and 3.6, 57% of the intervention group and 38% of controls made changes to treatment (p < 0.05). CONCLUSION: Small-group learning with feedback from practice audits is an inexpensive way to improve outcomes in RA.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".