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Record W2942641342 · doi:10.1186/s12874-019-0713-0

Strategies for eliciting and synthesizing evidence for guidelines in rare diseases

2019· article· en· W2942641342 on OpenAlexaff
Menaka Pai, Cindy H. T. Yeung, Elie A. Akl, Andrea Darzi, Christopher Hillis, Kimberly Legault, Joerg J Meerpohl, Nancy Santesso, Domenica Taruscio, Madeleine Verhovsek, Holger J. Schünemann, Alfonso Iorio

Bibliographic record

VenueBMC Medical Research Methodology · 2019
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsCochraneMcMaster UniversityHamilton Regional Laboratory Medicine ProgramHamilton General Hospital
Fundersnot available
KeywordsGuidelineMedicineDiseaseGrading (engineering)MEDLINEEvidence-based medicineClinical PracticeSystematic reviewEvidence-based practiceAlternative medicineFamily medicineIntensive care medicinePathologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Rare diseases are a global public health priority. Though each disease is rare, when taken together the thousands of known rare diseases cause significant morbidity and mortality, impact quality of life, and confer a social and economic burden on families and communities. These conditions are, by their nature, encountered very infrequently by individual clinicians, who may feel unprepared to address their diagnosis and treatment. Clinical practice guidelines are necessary to support clinical and policy decisions. However, creating guidelines for rare diseases presents specific challenges, including a paucity of high certainty evidence to inform panel recommendations. METHODS: This paper draws from the authors' experience in the development of clinical practice guidelines for three rare diseases: hemophilia, sickle cell disease, and catastrophic antiphospholipid syndrome. RESULTS: We have summarized a number of strategies for eliciting and synthesizing evidence that are compatible with the rigorous, internationally accepted standards for guideline development set out by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. These strategies include: use of pre-existing and ad hoc qualitative research, use of systematic observation forms, use of registry data, and thoughtful use of indirect evidence. Their use in three real guideline development efforts, as well as their theoretical underpinnings, are discussed. Avenues for future research to improve clinical practice guideline creation for rare diseases - and any disease affected by a relative lack of evidence - are also identified. CONCLUSIONS: Rigorous clinical practice guidelines are needed to improve the care of the millions of people worldwide who suffer from rare diseases. Innovative evidence elicitation and synthesis methods will benefit not only the rare disease community, but also individuals with common diseases who have rare presentations, suffer rare complications, or require nascent therapies. Further refinement and improved uptake of these innovative methods should lead to higher quality clinical practice guidelines in rare diseases.

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)0.020
metaresearch head score (Gemma)0.258
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.258
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.802
GPT teacher head0.656
Teacher spread0.146 · 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 designOther design
Domainnot available
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

Citations108
Published2019
Admission routes1
Has abstractyes

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