Evidence-Based Guideline: ACR made 10 strong treatment recommendations for RA, but high-quality evidence was sparse
Bibliographic record
Abstract
ACP Journal Club15 March 2016Evidence-Based Guideline: ACR made 10 strong treatment recommendations for RA, but high-quality evidence was sparseRobin Christensen, MSc, PhD, Lawrence E. Hart, MB BCh, MSc, FRCPCRobin Christensen, MSc, PhDMusculoskeletal Statistics Unit, The Parker Institute, Copenhagen, Denmark (R.C.)Search for more papers by this author, Lawrence E. Hart, MB BCh, MSc, FRCPCMcMaster University, Hamilton, Ontario, Canada (L.E.H.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJC-2016-164-6-027 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Source CitationSingh JA, Saag KG, Bridges SL Jr, et al. 2015 American College of Rheumatology guideline for the treatment of rheumatoid arthritis. Arthritis Rheumatol. 2016;68:1-26. https://pubmed.ncbi.nlm.nih.gov/26545940Clinical Impact RatingsGIM/FP/GP: Rheumatology: References1 Christensen R, Singh JA, Wells GA, Tugwell PS. Do “evidence-based recommendations” need to reveal the evidence? Minimal criteria supporting an “evidence claim” [Editorial]. J Rheumatol. 2015;42:1737-9. [PMID: 26429204] Google Scholar2 Neumann I, Santesso N, Akl EA, et al. A guide for health professionals to interpret and use recommendations in guidelines developed with the GRADE approach. J Clin Epidemiol. 2016;Jan 6. [Epub ahead of print.] [PMID: 26772609] Google Scholar3 Alexander PE, Gionfriddo MR, Li SA, et al. A number of factors explain why WHO guideline developers make strong recommendations inconsistent with GRADE guidance. J Clin Epidemiol. 2016;70:111-22. [PMID: 26399903] Google Scholar Author, Article, and Disclosure InformationAffiliations: Musculoskeletal Statistics Unit, The Parker Institute, Copenhagen, Denmark (R.C.)McMaster University, Hamilton, Ontario, Canada (L.E.H.)This article was published at Annals.org on 1 March 2016. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited byImportance of Shared Treatment Goal Discussions in Rheumatoid Arthritis—A Cross‐Sectional Survey: Patients Report Providers Seldom Discuss Treatment Goals and Outcomes Improve When Goals Are DiscussedPersistence with Early-Line Abatacept versus Tumor Necrosis Factor-Inhibitors for Rheumatoid Arthritis Complicated by Poor Prognostic FactorsPersistence with Early-Line Abatacept versus Tumor Necrosis Factor-Inhibitors for Rheumatoid Arthritis Complicated by Poor Prognostic FactorsMixed methods study of a new model of care for chronic disease: co-design and sustainable implementation of group consultations into clinical practice 15 March 2016Volume 164, Issue 6Page: JC27KeywordsDisease modifying antirheumatic drugsDrugsGrading of Recommendations Assessment Development and EvaluationPatient advocacyPatientsRheumatoid arthritisRheumatologySystematic reviewsTreatment guidelinesTumor necrosis factor ePublished: 15 March 2016 Issue Published: 15 March 2016 Copyright & PermissionsCopyright © 2016 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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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 | 0.027 | 0.143 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.021 | 0.015 |
| Insufficient payload (model declined to judge) | 0.028 | 0.026 |
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".