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Record W2625686186 · doi:10.1002/acr.23275

Clinical Practice Guidelines: Incorporating Input From a Patient Panel

2017· article· en· W2625686186 on OpenAlexaff
Susan M. Goodman, Amy S. Miller, Marat Turgunbaev, Gordon Guyatt, Adolph J. Yates, Bryan D. Springer, Jasvinder A. Singh

Bibliographic record

VenueArthritis Care & Research · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMcMaster University
FundersAmerican College of Rheumatology Research and Education Foundation
KeywordsMedicineGuidelinePerioperativePhysical therapyRheumatoid arthritisIntensive care medicineEvidence-based medicineSports medicinePanel discussionGrading (engineering)Internal medicineSurgeryAlternative medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the integral role of a Patient Panel in the development of the 2017 American College of Rheumatology (ACR)/American Association of Hip and Knee Surgeons (AAHKS) clinical practice guideline. METHODS: We convened a Panel of 11 patients with rheumatoid arthritis and juvenile idiopathic arthritis, all of whom had undergone 1 or more arthroplasties, to review the evidence and provide guidance on recommendations for the 2017 ACR/AAHKS guideline to address the perioperative management of antirheumatic medication in patients with rheumatic diseases undergoing elective total hip or total knee arthroplasty. The guideline used the Grading of Recommendations Assessment, Development, and Evaluation methodology that acknowledges the critical role of patient values and preferences when the quality of the evidence base is low or when there are important trade-offs between benefits and harms. The Patient Panel considered the relative importance of complications including perioperative infection versus rheumatic disease flare and voted on the recommendations. Before the Voting Panel's own discussion of the recommendations, they reviewed a summary of the Patient Panel's discussion, including their perioperative experience, the relative importance they placed on infections versus flares in the perioperative period, and their votes on the recommendations. RESULTS: The Patient Panel placed higher importance on avoiding an infection than a disease flare despite the far greater frequency of flares than infections. The decisions of the Voting Panel were concordant with those of the Patient Panel. For the 7 recommendations that both Panels voted on, the Panels agreed on the direction as well as the strength of recommendation (which was conditional for all recommendations). CONCLUSION: The Voting Panel considered the importance that the patients placed on risk of infection. The Patient Panel's values informed the direction and strength of the recommendations in the final 2017 ACR/AAHKS guideline.

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 imitation

Not 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.

metaresearch head score (Codex)0.277
metaresearch head score (Gemma)0.497
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.723
Threshold uncertainty score0.891

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2770.497
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0100.006
Science and technology studies0.0070.004
Scholarly communication0.0170.018
Open science0.0060.016
Research integrity0.0200.022
Insufficient payload (model declined to judge)0.0130.009

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 head0.615
GPT teacher head0.631
Teacher spread0.016 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
GenreEmpirical

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

Citations36
Published2017
Admission routes1
Has abstractyes

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