Next generation sequencing in psychiatric research: what study participants need to know about research findings
Bibliographic record
Abstract
The use of next generation sequencing (NGS) technologies in psychiatric genetics research and its potential to generate individual research results will likely have far reaching implications for predictive and diagnostic practices. The extent of this impact may not be easily understood by psychiatric research participants during the consent process. The traditional consent process for studies involving human subjects does not address critical issues specific to NGS research, such as the return of results. We examined which type of research findings should be communicated, how this information should be conveyed during the consent process and what guidance is required by researchers and IRBs to help psychiatric research participants understand the peculiarities, the limits and the impact of NGS. Strong standards are needed to ensure appropriate use of data generated by NGS, to meet participants' expectations and needs, and to clarify researchers' duties regarding the disclosure of data and their subsequent management. In the short term, researchers and IRBs need to be proactive in revising current consent processes that deal with the disclosure of research findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.117 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".