What else can I do but take drugs? The future of research in nonpharmacological treatment in early inflammatory arthritis.
Bibliographic record
Abstract
Nonpharmacological treatments, including physiotherapy and occupational therapy, have assumed a complementary role to drug therapy in managing inflammatory arthritis. Clinicians and researchers are facing 3 major challenges concerning the use of these treatments. First, strong evidence is only present in a few nonpharmacological interventions, such as exercise, patient education, and low level laser in the treatment of rheumatoid arthritis. The evidence on the majority of interventions is, however, weak or inconclusive. Second, knowledge is lacking on the elements associated with models of nonpharmacological care. The multidisciplinary team approach has been viewed as the standard for arthritis treatment; however, the team structure and the communication style among team members vary around the world. The influence of these elements on treatment success remains unclear. Finally, disparities in knowledge management and translation in nonpharmacological research have hindered the clinical use of these treatments and the growth of research in the field. To address the challenges, the author is recommending 4 research priorities for nonpharmacological treatments: 1. Evaluation of less well-studied interventions; 2. Understanding the relationships among rehabilitation-related variables and disability; 3. Development and evaluation of innovative care models; and 4. Design and evaluation of knowledge transfer innovations.
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 | 0.045 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".