Recommendations for the management of rheumatoid arthritis in the Eastern Mediterranean region: an adolopment of the 2015 American College of Rheumatology guidelines
Bibliographic record
Abstract
Clinical practice guidelines can assist rheumatologists in the proper prescription of newer treatment for rheumatoid arthritis (RA). The objective of this paper is to report on the recommendations for the management of patients with RA in the Eastern Mediterranean region. We adapted the 2015 American College of Rheumatology guidelines in two separate waves. We used the adolopment methodology, and followed the 18 steps of the "Guidelines 2.0" comprehensive checklist for guideline development. For each question, we updated the original guidelines' evidence synthesis, and we developed an Evidence Profile (EP) and an Evidence to Decision (EtD) table. In the first wave, we adoloped eight out of the 15 original questions on early RA. The strength changed for five of these recommendations from strong to conditional, due to one or more of the following factors: cost, impact on health equities, the balance of benefits, and harms and acceptability. In the second wave, we adoloped eight out of the original 44 questions on established RA. The strength changed for two of these recommendations from strong to conditional, in both cases due to cost, impact on health equities, balance of benefits and harms, and acceptability. The panel also developed a good practice recommendation. We successfully adoloped 16 recommendations for the management of early and established RA in the Eastern Mediterranean region. The process proved feasible and sensitive to contextual factors.
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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 | 0.062 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".