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Record W4327682584 · doi:10.1186/s42358-023-00293-4

Brazilian society of rheumatology methodological guide for the development of evidence-based clinical guidelines in rheumatology

2023· review· en· W4327682584 on OpenAlexaff
Ana Karla Guedes de Melo, Ana Luíza Mendes Amorim Caparroz, Mirhelen Mendes de Abreu, Daniela Castelo Azevedo, Leonardo Santos Hoff, Sérgio Cândido Kowalski, Themis Mizerkowski Torres, Solange Murta Barros, Gilda Aparecida Ferreira, Odirlei André Montecielo, Ricardo Machado Xavier, Virgínia Fernandes Moça Trevisani

Bibliographic record

VenueAdvances in Rheumatology · 2023
Typereview
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsRheumatologyInternal medicineMedicineFamily medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.127
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.919
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.127
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.816
GPT teacher head0.674
Teacher spread0.141 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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