Exploring neurologists’ perspectives on the return of next generation sequencing results to their patients: a needed step in the development of guidelines
Bibliographic record
Abstract
BACKGROUND: The use of Next Generation Sequencing such as Whole Genome Sequencing (WGS) is a promising step towards a better understanding and treatment of neurological diseases. WGS can result into unexpected information (incidental findings, IFs), and information with uncertain clinical significance. In the context of a Genome Canada project on 'Personalized Medicine in the Treatment of Epilepsy', we intended to address these challenges surveying neurologists' opinions about the type of results that should be returned, and their professional responsibility toward recontacting patients regarding new discovered mutations. METHODS: Potential participants were contacted through professional organizations or direct invitations. RESULTS: A total of 204 neurologists were recruited. Fifty nine percent indicated that to be conveyed, WGS results should have a demonstrated clinical utility for diagnosis, prognosis or treatment. Yet, 41% deemed appropriate to return results without clinical utility, when they could impact patients' reproductive decisions, or on patients' request. Current use of targeted genetic testing and age of patients influenced respondents' answers. Respondents stated that analysis of genomics data resulting from WGS should be limited to the genes likely to be relevant for the patient's specific medical condition (69%), so as to limit IFs. Respondents felt responsible to recontact patients and inform them about newly discovered mutations related to the medical condition that triggered the test (75%) for as long as they are following up on the patient (55%). Finally, 53.5% of the respondents felt responsible to recontact and inform patients of clinically significant, newly discovered IFs. CONCLUSION: Our results show the importance of formulating professional guidelines sensitive to the various - and sometimes opposite - viewpoints that may prevail within a same community of practice, as well as flexible so as to be attuned to the characteristics of the neurological conditions that triggered a WGS.
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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 | 0.051 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".