A qualitative study among guideline developers revealed challenges and strategies for rare disease guideline development
Bibliographic record
Abstract
OBJECTIVES: Clinical practice guideline (CPG) development for rare diseases is challenging due to scarce evidence, small expert groups, limited resources, and heterogeneity and complexity of conditions. Critical appraisals of existing rare disease CPGs reveal variable methodological quality. We aimed to gather the experiences of rare disease guideline developers to identify methodological challenges and strategies and eventually inform methodological guidance for rare disease CPGs. STUDY DESIGN AND SETTING: We conducted semistructured interviews with 15 guideline developers from ten countries and diverse medical fields with hands-on experience in rare disease CPG development. Data were analyzed through a combined deductive and inductive approach following the structure of the GIN-McMaster Guideline Development Checklist. RESULTS: Small rare disease expert groups, while highly dedicated, faced significant risks related to conflicts of interest, limited methodological expertise, resource constraints, and challenges in achieving interest-holder representation. Guideline developers adopted pragmatic approaches to utilize scarce and very low-certainty direct evidence and supplement it with indirect and expert-based evidence, registry data, and mechanistic reasoning. The Grading of Recommendations Assessment, Development and Evaluation methodology was valued for providing transparency, structure, and consistency, but some considered it not feasible in rare disease contexts. Topics beyond the GIN-McMaster Guideline Development Checklist included deciding whether to develop a CPG or another type of quality document and supporting the broader knowledge cycle. CONCLUSION: We gained insight into the most salient methodological issues and identified a need for further guidance and method development to improve guideline development processes for rare diseases.
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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 | 0.076 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".