Back to Basics: Prioritizing Communication as a Key Instrument in Managing Rheumatoid Arthritis
Bibliographic record
Abstract
Patients with rheumatoid arthritis (RA) have come to experience a tremendous increase in therapeutic options with disease-modifying antirheumatic drugs (DMARDs).1 After decades of dissatisfying drug therapy results with conventional synthetic DMARDs (csDMARDs) only, the introduction of the first tumor necrosis factor inhibitors in the late 1990s has revolutionized RA treatment.2 Over the last 3 decades, different biologic DMARDs (bDMARDs) and targeted synthetic DMARDs (tsDMARDs) have been administered, targeting different disease mechanisms and showing efficacy after failure of csDMARDs. These manifold opportunities were then required to be framed in a structured management plan such as the treat-to-target (T2T) recommendations in 2010.3 Treatment of RA was established, with suggestions of adequate holistic measurement of disease activity.4,5 The inclusion of the patient perspective was integrated by using patient-reported outcome measures (PROMs) as part of the assessments.6,7,8 With improved therapeutic options, treatment goals have changed. The patient– healthcare professional (HCP) team strives for achieving and maintaining clinical remission, which has become a realistic goal for many patients with RA.9,10 However, pharmacological treatment alone will not be sufficient to address all the layers affected by RA and to improve disease activity and RA-related health issues. Even in recent randomized controlled phase III trials of novel tsDMARDs, Boolean or Simplified Disease Activity Index remission rates are approximately 20% after 6 months.11,12,13 The majority of these trial patients can be classified as being in a state of low disease activity (LDA), demonstrating the efficacy of therapy but underlining … Address correspondence to Dr. P. Studenic, Karolinska Institutet, Department of Medicine (Solna), Division of Rheumatology, D2:01, Karolinska Universitetssjukhuset, 171 76 Stockholm, Sweden. Email: Paul.studenic{at}muv.ac.at.
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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 | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.022 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".