Methodology for the adolopment of recommendations for the treatment of rheumatoid arthritis in the Kingdom of Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Currently, there are no guidelines for the treatment of rheumatoid arthritis (RA) tailored to the context of the Kingdom of Saudi Arabia (KSA). Adaptation of guidelines accounts for contextual factors and becomes more efficient than de novo guideline development when relevant, good quality, and up-to-date guidelines are available. The objective of this study is to describe the methodology used for the adolopment of the 2021 American College of Rheumatology (ACR) guidelines for the treatment of RA in the KSA. METHODS: We followed the 'Grading of Recommendations Assessment, Development and Evaluation' (GRADE)-ADOLOPMENT methodology. The adolopment KSA panel included relevant stakeholders and leading contributors to the original guidelines. We developed a list of five adaptation-relevant prioritization criteria that the panelists applied to the original recommendations. We updated the original evidence profiles with newly published studies identified by the panelists. We constructed Evidence to Decision (EtD) tables including contextual information from the KSA setting. We used the PanelVoice function of GRADEPro Guideline Development Tool (GDT) to obtain the panel's judgments on the EtD criteria ahead of the panel meeting. Following the meeting, we used the PANELVIEW instrument to obtain the panel's evaluation of the process. RESULTS: The KSA panel prioritized five recommendations, for which one evidence profile required updating. Out of five adoloped recommendations, two were modified in terms of direction, and one was modified in terms of certainty of the evidence. Criteria driving the modifications in direction were valuation of outcomes, balance of effects, cost, and acceptability. The mean score on the 7-point scale items of the PANELVIEW instrument had an average of 6.47 (SD = 0.18) across all items. CONCLUSION: The GRADE-ADOLOPMENT methodology proved to be efficient. The panel assessed the process and outcome positively. Engagement of stakeholders proved to be important for the success of this project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.118 | 0.400 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".