Locus-specific databases: from ethical principles to practice
Bibliographic record
Abstract
Locus-specific databases (LSDBs) play an essential role in clinical care and research. They differ from traditional genetic databases in that they propose to place the mutations of "anonymized" patients directly on the World Wide Web. The proliferation of ethical guidelines and legal requirements affects the rapid and free transmission of clinical data, which is vital for both the daily management of patients and research into better diagnostics and treatment. This paper proposes a review of ethical principles endorsed by international instruments that are of particular relevance to LSDBs. It aims to translate them into 12 proposed practical guidelines that LSDB curators can use in collecting data for clinical research. Perhaps these guideposts will serve as a first step toward translating principles into practice.
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 | 0.220 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".