Implementing evidence-based assertions of clinical actionability in the context of secondary findings: Updates from the ClinGen Actionability Working Group
Bibliographic record
Abstract
PURPOSE: The ClinGen Actionability Working Group (AWG) developed an evidence-based framework to generate actionability reports and scores of gene-condition pairs in the context of secondary findings from genome sequencing. Here we describe the expansion of the framework to include actionability assertions. METHODS: Initial development of the actionability rubric was based on previously scored adult gene-condition pairs and individual expert evaluation. Rubric refinement was iterative and based on evaluation, feedback, and discussion. The final rubric was pragmatically evaluated via integration into actionability assessments for 27 gene-condition pairs. RESULTS: The resulting rubric has a 4-point scale (limited, moderate, strong, and definitive) and uses the highest-scoring outcome-intervention pair of each gene-condition pair to generate a preliminary assertion. During AWG discussions, predefined criteria and factors guide discussion to produce a consensus assertion for a gene-condition pair, which may differ from the preliminary assertion. The AWG has retrospectively generated assertions for all previously scored gene-condition pairs and are prospectively asserting on gene-condition pairs under assessment, having completed over 170 adult and 188 pediatric gene-condition pairs. CONCLUSION: The AWG expanded its framework to provide actionability assertions to enhance the clinical value of their resources and increase their utility as decision aids regarding return of secondary findings.
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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 | 0.600 | 0.775 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.022 | 0.028 |
| Research integrity | 0.036 | 0.053 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".