Managing Incidental Findings in Human Subjects Research: Analysis and Recommendations
Bibliographic record
Abstract
No consensus yet exists on how to handle incidental findings (IFs) in human subjects research. Yet empirical studies document IFs in a wide range of research studies, where IFs are findings beyond the aims of the study that are of potential health or reproductive importance to the individual research participant. This paper reports recommendations of a two-year project group funded by NIH to study how to manage IFs in genetic and genomic research, as well as imaging research. We conclude that researchers have an obligation to address the possibility of discovering IFs in their protocol and communications with the IRB, and in their consent forms and communications with research participants. Researchers should establish a pathway for handling IFs and communicate that to the IRB and research participants. We recommend a pathway and categorize IFs into those that must be disclosed to research participants, those that may be disclosed, and those that should not be disclosed.
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 | 0.323 | 0.588 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".