E55. RHEUMATOLOGISTS’ PERCEPTIONS IMPACT CLINICAL DECISION MAKING WHEN COMMENCING NEW PATIENTS WITH RHEUMATOID ARTHRITIS ON METHOTREXATE
Bibliographic record
Abstract
Background: Very little is known about the medication and treatment beliefs of rheumatologists and how these beliefs might impact practice. In a preceding qualitative study the authors identified a number of factors that may contribute to the discussion and commencement of methotrexate (MTX) for the treatment of rheumatoid arthritis (RA). This study aimed to examine if rheumatologists’ decision to discuss or commence MTX during the initial consultation associated with the gender, perceived emotional preparedness, or perceived prior knowledge of the patient. Methods: This was the second phase of a sequential, exploratory mixed methods designed study. An online survey was designed and refined based on interviews with UK rheumatologists. The questionnaire included a factorial survey. Two patient vignettes manipulated the following patient factors; male/female, emotionally prepared/unprepared and no/negative prior knowledge. Rheumatologists were asked to judge for each vignette the likelihood of Commencing MTX (0=very unlikely – 10=very likely). Rheumatologists, rated how important it was to discuss specific pieces of MTX information (4=very important – 1 = not important at all), the Discussing MTX variable was obtained from the mean value. A MANCOVA tested if each level of gender × patient preparedness × prior knowledge, controlling for individual differences in scoring, associated with 1) Commencing MTX and 2) Discussing MTX. Pillai’s Trace (V) measured the overall fit of the model and the main and interaction effects on the dependent variables. Post hoc, univariate analyses were conducted to identify the nature of effects (p <0.05).
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".