Shortened consent forms for genome‐wide sequencing: Parent and provider perspectives
Bibliographic record
Abstract
BACKGROUND: Consent forms for exome and/or genome sequencing, collectively called genome-wide sequencing (GWS), frequently contain detailed information on complex topics such as sequencing analysis and incidental findings. Considering recent endeavors by the health care community to simplify GWS consent forms, it is important to gain stakeholders' perspectives on the content, length, and use of consent forms. METHODS: Thematic analysis was conducted on data obtained from focus groups with two participant cohorts: parents who previously provided consent for trio-based GWS as part of the translational pediatric GWS CAUSES Study, and genetic health care providers (HCP) who provide pre-test counseling for GWS. RESULTS: Genetic HCP indicated that consent forms cannot replace pre-test counseling, and as such, a simplified consent form focusing on the implications of GWS would be beneficial to both patients and HCP. Although parents' primary concerns varied when considering GWS, they all highly valued information. Parents also indicated the need for community and support after the return of GWS results. Both participant cohorts recommended that consent forms be available online and include an appendix for supplementary information. CONCLUSION: It is important to include both parents and HCP in the design of GWS consent forms, and also, to help connect families who have a shared diagnosis after the post-test counseling session.
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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 | 0.166 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".