Heath policy guiding the identification, analysis and management of secondary findings for individuals undergoing genomic sequencing: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Genomic sequencing is increasingly enabling precision care across medical specialties; however, the discovery of genomic 'secondary findings' (SFs) unrelated to the patient's primary indication remains a profuse, unintended consequence. Existing practices within the continuum of SF identification, analysis and management are numerous, inconsistent and sometimes contradictory across health conditions and regions. Final decisions are often at the discretion of the genomic sequencing laboratory, bioinformatician or treating physician. This difference in healthcare delivery causes inconsistent information, disclosure and downstream impacts required to manage SFs and patient outcomes. Improving our understanding of the SF health policy landscape can determine components of the SF policy continuum spanning generation through to management that are in conflict, limitations of current guidance and existing needs across clinical settings. METHODS AND ANALYSIS: We will carry out a systematic review to catalogue and appraise current guidance directing the identification, analysis and management of SFs for participants receiving genomic sequencing globally. We will conduct a comprehensive search of Medline (Medline R, Medline Epub Ahead of Print and Medline-In-Process & In-Data-Review Citations), Embase and Cochrane databases (n=5, inception to Feb 2022) and a grey literature search of international genomics websites (n=64; inception to May 2022). Key inclusion criteria include: guidance produced by health organisations, bioethics committees and professional associations, outlining recommendations for: (1) SF identification, (2) SF analysis or (3) SF management. Non-English language articles and conference abstracts will be excluded. Guidance will be critically appraised with the Appraisal of Guidelines for Research & Evaluation Instrument (AGREE) II tool. We will interpret our findings by process and across populations using a qualitative descriptive approach. ETHICS AND DISSEMINATION: Our systematic review evaluates published data and does not require ethics review. Our findings will be disseminated through peer-reviewed publications, conference presentations and workshops with precision medicine stakeholders. PROSPERO REGISTRATION NUMBER: CRD42022316079.
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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 | 0.123 | 0.140 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".