Treating Rheumatoid Arthritis to Target: A Canadian Physician Survey
Bibliographic record
Abstract
OBJECTIVE: To assess agreement and application of Treat to Target (T2T) recommendations in Canadian practice. METHODS: A survey of Canadian rheumatologists was conducted on the recommendations of T2T, an international initiative toward reaching specific therapeutic goals in rheumatoid arthritis. Agreement with each recommendation was measured on a 10-point Likert scale (1 = fully disagree, 10 = fully agree). A 4-point Likert scale (never, not very often, very often, always) assessed application of each recommendation in current practice. Responders who answered "never" or "not very often" were asked whether they were willing to change their practice according to the particular recommendation. RESULTS: Seventy-eight rheumatologists responded (24% of the 330 who were contacted). The average agreement scores ranged from 6.92 for recommendation #5 (the frequency of measures of disease activity) to 9.10 for recommendation #10 (the patient needs to be involved in the decision-making process). A majority of participants indicated that they apply the T2T recommendations in their practice. Recommendations dealing with frequency of visits and the use of composite measures received the highest number of "never" or "not very often" responses. Busy practices and lack of confidence in composite outcome measures were the main reasons for objections to certain components of the recommendations. CONCLUSION: Although a majority of Canadian rheumatologists agreed with and supported the T2T recommendations, there was resistance toward specific aspects of these recommendations. Efforts are needed to better understand the reasons behind identified disagreements. Action plans to encourage the application of T2T recommendations in Canada are in development.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".