A comprehensive genomic reporting structure for communicating all clinically significant primary and secondary findings
Bibliographic record
Abstract
Abstract Genomic sequencing (GS) can reveal secondary findings (SFs), findings unrelated to the reason for testing, that can be overwhelming to both patients and providers. An effective approach for communicating all clinically significant primary and secondary GS results is needed to effectively manage this large volume of results. The aim of this study was to develop a comprehensive approach to communicate all clinically significant primary and SF results. A genomic test report with accompanying patient and provider letters were developed in 3 phases: review of current clinical reporting practices, consulting with genetic and non-genetics experts, and iterative refinement through circulation to key stakeholders. The genomic test report and consultation letters present a myriad of clinically relevant GS results in distinct, tabulated sections, including primary and secondary findings, with in-depth details of each finding. They provide detailed variant and disease information, personal and familial risk assessments, clinical management details, and additional resources to help support providers and patients with implementing healthcare recommendations related to their GS results. The report and consultation letters represent a comprehensive approach to communicate all clinically significant SFs to patients and providers, facilitating clinical management of GS results.
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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 | 0.085 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.018 |
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".