A Proposed Research Agenda for Ethical, Legal, Social, and Historical Studies at the Intersection of Infectious and Genetic Disease
Bibliographic record
Abstract
Over the past two decades there has been a rapid expansion in our understanding of how human genetic variability impacts susceptibility and severity of disease. Through applications of genome-wide association studies, genome and exome sequencing, researchers have made thousands of discoveries of genetic variants that impact risk of common and rare disorders affecting millions of people. Although these techniques have been primarily applied to highly prevalent chronic disorders such as diabetes1 and cardiovascular disease2, infectious diseases have proven to not be immune to genome-wide association, with studies of Tuberculosis3, HIV4 and SARS-CoV25, to name but a few, identifying host susceptibility loci across the genome. Unlike non-communicable diseases, infectious diseases have the unique element of impacting not only the affected the host, but those who may be most vulnerable to acquiring the infection. Thus, genetic variants that impact one individual’s susceptibility to and severity of an infection may also have broader implications to public health, as was brought into keen focus during the COVID-19 pandemic. Therefore, as we begin to apply the knowledge gained from genomic studies in the clinic or into policy, there are unique ethical, legal, and social implications (ELSI) at the intersection of infectious diseases and human genomics. In this issue of the Journal of Law, Medicine and Ethics, Jose et al attempt to address this need by proposing a research agenda for ELSI studies at what they term the “blurred boundaries” of infectious and genetic diseases.6
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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 | 0.156 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.024 | 0.083 |
| Scholarly communication | 0.037 | 0.051 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.077 | 0.069 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".