Treating rheumatoid arthritis to target: 2014 update of the recommendations of an international task force
Bibliographic record
Abstract
BACKGROUND: Reaching the therapeutic target of remission or low-disease activity has improved outcomes in patients with rheumatoid arthritis (RA) significantly. The treat-to-target recommendations, formulated in 2010, have provided a basis for implementation of a strategic approach towards this therapeutic goal in routine clinical practice, but these recommendations need to be re-evaluated for appropriateness and practicability in the light of new insights. OBJECTIVE: To update the 2010 treat-to-target recommendations based on systematic literature reviews (SLR) and expert opinion. METHODS: A task force of rheumatologists, patients and a nurse specialist assessed the SLR results and evaluated the individual items of the 2010 recommendations accordingly, reformulating many of the items. These were subsequently discussed, amended and voted upon by >40 experts, including 5 patients, from various regions of the world. Levels of evidence, strengths of recommendations and levels of agreement were derived. RESULTS: The update resulted in 4 overarching principles and 10 recommendations. The previous recommendations were partly adapted and their order changed as deemed appropriate in terms of importance in the view of the experts. The SLR had now provided also data for the effectiveness of targeting low-disease activity or remission in established rather than only early disease. The role of comorbidities, including their potential to preclude treatment intensification, was highlighted more strongly than before. The treatment aim was again defined as remission with low-disease activity being an alternative goal especially in patients with long-standing disease. Regular follow-up (every 1-3 months during active disease) with according therapeutic adaptations to reach the desired state was recommended. Follow-up examinations ought to employ composite measures of disease activity that include joint counts. Additional items provide further details for particular aspects of the disease, especially comorbidity and shared decision-making with the patient. Levels of evidence had increased for many items compared with the 2010 recommendations, and levels of agreement were very high for most of the individual recommendations (≥9/10). CONCLUSIONS: The 4 overarching principles and 10 recommendations are based on stronger evidence than before and are supposed to inform patients, rheumatologists and other stakeholders about strategies to reach optimal outcomes of 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 | 0.074 | 0.137 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".