Exome and genome sequencing for rare genetic disease diagnosis: A scoping review and critical appraisal of clinical guidance documents produced by genetics professional organizations
Bibliographic record
Abstract
PURPOSE: Exome and genome sequencing have rapidly transitioned from research methods to widely used clinical tests for diagnosing rare genetic diseases. We sought to synthesize the topics covered and appraise the development processes of clinical guidance documents generated by genetics professional organizations. METHODS: We conducted a scoping review of guidance documents published since 2010, systematically identified in peer-reviewed and gray literature, using established methods and reporting guidelines. We coded verbatim recommendations by topic using content analysis and critically appraised documents using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool. RESULTS: We identified 30 guidance documents produced by 8 organizations (2012-2022), yielding 611 recommendations covering 21 topics. The most common topic related to findings beyond the primary testing indication. Mean AGREE II scores were low across all 6 quality domains; scores for items related to rigor of development were among the lowest. More recently published documents generally received higher scores. CONCLUSION: Guidance documents included a broad range of recommendations but were of low quality, particularly in their rigor of development. Developers should consider using tools such as AGREE II and basing recommendations on living knowledge syntheses to improve guidance development in this evolving space.
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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 | 0.099 | 0.263 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.027 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".