Disclosure of incidental findings in cancer genomic research: investigators' perceptions on obligations and barriers
Bibliographic record
Abstract
Although there has been significant research surrounding incidental findings (IFs), the guidelines and information provided to investigators remain unspecific, unclear, and often generalize the course of action to everyone in the field. We explored the perceptions and experiences of investigators regarding the return of IFs in genetic research. Researchers and clinician-researchers were invited to participate in semi-structured telephone interviews in Quebec and Ontario. Twenty professionals participated, and thematic analysis was used to analyze the transcriptions. Four contextual elements emerged: (i) degree of significance of results, (ii) respect for persons, (iii) infrastructure implications, and (iv) professional responsibilities. Our findings demonstrate that all investigators raised similar contextual elements surrounding the return of IFs. However, some nuances in participants' experiences of the understanding of professional responsibilities also emerged. Because of the existing nuances, a one-size-fits-all approach is inappropriate, suggesting that context ought to be considered in decisions about IFs.
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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 | 0.183 | 0.307 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".