Influence of an Educational Seminar on Use of Disease Activity Measurements by Rheumatologists in Treatment of Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the variables underlying clinical decisions made by rheumatologists when treating patients with rheumatoid arthritis (RA), and to determine the effect of an educational seminar on the use of quantitative disease activity measurements in clinical practice in this population of physicians. METHODS: Practicing rheumatologists were surveyed on the variables affecting their clinical management of patients with RA by questionnaire. Physicians were divided into 2 groups: the first comprised attenders (Group A) to an educational seminar in the use of the quantitative disease activity measurements in patient management, while the second group comprised nonattenders (Group NA). Both groups were surveyed on their practice behavior before (Survey 1) and 2 to 3 months after (Survey 2) the seminar. RESULTS: Fifty-two rheumatologists in clinical practice from across the US completed and returned 364 surveys. A significantly greater number of rheumatologists in Group A reported use of disease activity measures following the training seminar (Survey 2), compared to their use pre-meeting and compared to Group NA (p < 0.0001). CONCLUSION: Our results support employment of an educational seminar on the use of disease activity measurements to increase the use of these quantitative measures in rheumatologic practice.
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".