Old Challenges or New Issues? Genetic Health Professionals’ Experiences Obtaining Informed Consent in Diagnostic Genomic Sequencing
Bibliographic record
Abstract
BACKGROUND: While integrating genomic sequencing into clinical care carries clear medical benefits, it also raises difficult ethical questions. Compared to traditional sequencing technologies, genomic sequencing and analysis is more likely to identify unsolicited findings (UF) and variants that cannot be classified as benign or disease-causing (variants of uncertain significance; VUS). UF and VUS pose new challenges for genetic health professionals (GHPs) who are obtaining informed consent for genomic sequencing from patients. METHODS: We conducted semi-structured interviews with 31 GHPs across Europe, Australia and Canada to identify some of these challenges. RESULTS: Our results show that GHPs find it difficult to prepare patients to receive results because a vast amount of information is required to fully inform patients about VUS and UF. GHPs also struggle to engage patients - many of whom may be focused on ending their 'diagnostic odyssey' - in the informed consent process in a meaningful way. Thus, some questioned how 'informed' patients actually are when they agree to undergo clinical genomic sequencing. CONCLUSIONS: These findings suggest a tension remains between sufficient information provision at the risk of overwhelming the patient and imparting less information at the risk of uninformed decision-making. We suggest that a shift away from 'fully informed consent' toward an approach aimed at realizing, as far as possible, the underlying goals that informed consent is meant to promote.
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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 | 0.091 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.045 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".