Influence of Demographic Characteristics of Participants on Consent to Genomic Research into Congenital Heart Disease
Bibliographic record
Abstract
Background: The enrollment of sick children and their families in genomics studies calls for a comprehensive view of the consent process.Few studies have searched for correlations between the demographic characteristics of participants (age, gender, parental lineage) or their level of participation (affected children, parents, or other relatives), on the one hand, and patterns of consent to specific pediatric research procedures, on the other (DNA banking, use of cardiac tissue, disclosure of a cardiac condition, creation of cell lines, recall of a participant).Objectives: This study sought to analyze the extent to which respondents' participation in genomic research into congenital heart disease, based on their consent to specific procedures, revealed patterns correlated with their demographic data.Methods and findings: Data were abstracted from consent forms obtained from 600 participants enrolled in a research project on the genomics of congenital heart disease.Results: Our analysis revealed significant patterns between demographic characteristics and willingness to consent to various aspects of genomic research into congenital heart disease.Conclusions: Participant heterogeneity needs to be considered by clinical researchers in order to identify specific sub-groups of participants who may require more attention for improving the recruitment and retention in genomic research into congenital heart disease, as well as the consent process.
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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 | 0.053 | 0.246 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".