Brazilian society of rheumatology methodological guide for the development of evidence-based clinical guidelines in rheumatology
Bibliographic record
Abstract
Clinical practice guidelines (CPG) are developed to align standards of health care around the world, aiming to reduce the incidence of misconducts and enabling more effective use of health resources. Considering the complexity, cost, and time involved in formulating CPG, strategies should be used to facilitate and guide authors through each step of this process. The main objective of this document is to present a methodological guide prepared by the Epidemiology Committee of the Brazilian Society of Rheumatology for the elaboration of CPG in rheumatology. Through an extensive review of the literature, this study compiles the main practical recommendations regarding the following steps of CPG drafting: distribution of working groups, development of the research question, search, identification and selection of relevant studies, evidence synthesis and quality assessment of the body of evidence, the Delphi methodology for consensus achievement, presentation and dissemination of the recommendations, CPG quality assessment and updating. This methodological guide serves as an important tool for rheumatologists to develop reliable and high-quality CPG, standardizing clinical practices worldwide.
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 | 0.239 | 0.422 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.031 | 0.027 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier 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".