Developing multidisciplinary guidelines for the management of early rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: To develop an evidence based guideline, for the multidisciplinary management of early rheumatoid arthritis (RA). METHODS: Recommendations were developed using both an evidence-based approach and expert opinion. The scientific committee, composed of key members of the rheumatology multidisciplinary team used a Delphi approach to evaluate topics and standard statements, which formed the basis for developing recommendations for management of RA in the first 2 years of disease. Evidence taken from literature was used to support these recommendations. RESULTS: 24 evidence based recommendations for the management of early RA, with a grade of recommendation from A to C, were developed. In addition an algorithm of care was designed to promote a clear multidisciplinary management pathway. A mechanism for audit was also identified. CONCLUSION: Involvement of the multidisciplinary rheumatology team has enabled a holistic guideline to be developed for the management of patients presenting with early RA. This guideline is based around best practice that is supported by published literature. Whilst most statements in the guideline are based on strong evidence, others have been formulated by expert consensus in the absence of data and should serve as an opportunity to improve current practice through future research and audit. The development and implementation of such a guideline should improve the care of patients with early RA.
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 | 0.033 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".