Ethical challenges in pathogen sequencing: a systematic scoping review
Bibliographic record
Abstract
Background : Going forward, the routine implementation of genomic surveillance activities and outbreak investigation is to be expected. We sought to systematically identify the emerging ethical challenges; and to systematically assess the gaps in ethical frameworks or thinking and identify where further work is needed to solve practical challenges. Methods : We systematically searched indexed academic literature from PubMed, Google Scholar, and Web of Science from 2000 to April 2019 for peer-reviewed articles that substantively engaged in discussion of ethical issues in the use of pathogen genome sequencing technologies for diagnostic, surveillance and outbreak investigation. Results : 28 articles were identified; nine United States, five United Kingdom, five The Netherlands, three Canada, two Switzerland, one Australia, two South Africa, and one Italy. Eight articles were specifically about the use of sequencing in HIV. Eleven were not specific to a particular disease. Results were organized into four themes: tensions between public and private interests; difficulties with translation from research to clinical and public health practice; the importance of community trust and support; equity and global partnerships; and the importance of context. Conclusion : While pathogen sequencing has the potential to be transformative for public health, there are a number of key ethical issues that must be addressed, particularly around the conditions of use for pathogen sequence data. Ethical standards should be informed by public values, and further empirical work investigating stakeholders’ views are required. Development in the field should also be under-pinned by a strong commitment to values of justice, in particular global health equity.
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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 | 0.142 | 0.414 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.034 | 0.029 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| 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".