Adaptation of the 2015 American College of Rheumatology treatment guideline for rheumatoid arthritis for the Eastern Mediterranean Region: an exemplar of the GRADE Adolopment
Bibliographic record
Abstract
BACKGROUND: It has been hypothesized that adaptation of health practice guidelines to the local setting is expected to improve their uptake and implementation while cutting on required resources. We recently adapted the published American College of Rheumatology (ACR) Rheumatoid Arthritis (RA) treatment guideline to the Eastern Mediterranean Region (EMR). The objective of this paper is to describe the process used for the adaptation of the 2015 ACR guideline on the treatment of RA for the EMR. METHODS: We used the GRADE-Adolopment methodology for the guideline adaptation process. We describe in detail how adolopment enhanced the efficiency of the following steps of the guideline adaptation process: (1) groups and roles, (2) selecting guideline topics, (3) identifying and training guideline panelists, (4) prioritizing questions and outcomes, (5) identifying, updating or conducting systematic reviews, (6) preparing GRADE evidence tables and EtD frameworks, (7) formulating and grading strength of recommendations, (8) using the GRADEpro-GDT software. RESULTS: The adolopment process took 6 months from January to June 2016 with a project coordinator dedicating 40% of her time, and the two co-chairs dedicating 5% and 10% of their times respectively. In addition, a research assistant worked 60% of her time over the last 3 months of the project. We held our face-to-face panel meeting in Qatar. Our literature update included five newly published trials. The certainty of the evidence of three of the eight recommendations changed: one from moderate to very low and two from low to very low. The factors that justified a very low certainty of the evidence in the three recommendations were: serious risk of bias and very serious imprecision. The strength of five of the recommendations changed from strong to conditional. The factors that justified the conditional strength of these 5 recommendations were: cost (n = 5 [100%]), impact on health equities (n = 4 [80%]), the balance of benefits and harms (n = 1 [20%]) and acceptability (n = 1 [20%]). CONCLUSION: This project confirmed the feasibility of GRADE-Adolopment. It also highlighted the value of collaboration with the organization that had originally developed the treatment guideline. We discuss the implications for both guideline adaptation and future research to advance the field.
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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 | 0.089 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".