224. HOW MUCH IS TOO MUCH? RHEUMATOLOGISTS’ VIEWS ON DISCUSSING METHOTREXATE FOR THE TREATMENT OF RHEUMATOID ARTHRITIS WITH PATIENTS
Bibliographic record
Abstract
Background: Rheumatologists are the primary prescribers of MTX for the treatment of rheumatoid arthritis (RA) in the United Kingdom; however their views about MTX are largely unknown. In a previous qualitative study rheumatologists highlighted a number of factors that contributed to their ability to discuss and commence MTX, which included how cognitively prepared patients were to discuss treatments. The aim of this study was to further explore these themes and investigate how rheumatologists inform patients during clinical consultations. Methods: This is the second phase of a sequential exploratory mixed methodology design. An online survey was designed to investigate UK rheumatologists’ views about MTX, and refined based on a qualitative study with rheumatologists in the UK. The survey asked rheumatologists to describe their clinic (multidisciplinary staff, drug education, time spent with patients) and their typical MTX regimen. Rheumatologists were presented with a series of MTX and RA related issues and asked how important it was for them to discuss each one with the patient during a consultation to commence MTX (5=Always to 1 = never). They were also asked “If you do not always discuss these issues can you identify the reasons why?” and provided with a pre-defined response set and free text option to select from.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".