Bibliographic record
Abstract
e field of genomic medicine moves quickly, and previously unrealized ethical issues are becoming apparent as new technologies are invented, novel data emerge and clinical applications are established.In the three years since the launch of Genome Medicine, we have seen discussion panels at genomics conferences tackling such topics as return of research results to participants and physician education in the genomic era, while headlines have been made by the Genetic Information Nondiscrimination Act [1], the Myriad Genetics patent litigation [2], and the US Food and Drug Administration's take on direct-to-consumer testing [3].Considerations of the ethical implications of genomic technologies and data are crucial to the implementation of genomic advances for improving human health.Work in the ethical, legal and social issues (ELSI) field does not always fit the standard Research article format, but broad, transparent access to these analyses and discussions is essential if they are to be integrated into the field of genomic medicine.In this issue of the journal, we introduce a peer-reviewed open-access article type, Open Debate, to accommodate novel and scholarly arguments that are informed by analysis and discussion, not just practical research.In the first article of this new type, Timothy Caulfield, Shawn Harmon and Yann Joly investi gate the conflicts between open science and commerciali zation in genomics research [4].Genomics has a culture of open data dating to before the time of the Human Genome Project, with the principles of accessibility formalized in the Bermuda agreement leading to an ongoing general principle that sequencing data should be released into the public domain as soon as possible after they are generated, and at least prior to publication.In the burgeoning field of medical sequen cing, this principle has been tempered somewhat by considerations of patient privacy (a topic for another day), but as a rule, openness in genomics research con tinues to be both integral and essential.For example, it is hard to imagine how the recent explosion of genome-wide association studies, with their huge
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.
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 | 0.079 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.092 |
| Scholarly communication | 0.055 | 0.065 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.055 | 0.063 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".