Managing Incidental Findings in Human Subjects Research: Analysis and Recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.040 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it