Introduction: the why and whither of genomic data sharing
Bibliographic record
Abstract
The Global Alliance for Genomics and Health (GA4GH) has estimated that, by the end of 2018, over 20% of genome and exome sequencing will be within and funded by healthcare systems for possible use in what can be termed “genomic medicine” ( https://www.ga4gh.org ). By 2030, it foresees that 83,000,000 rare-disease genomes will have been sequenced for diagnosis and 248,000,000 genomes will have been sequenced for cancer diagnosis (Birney et al. 2017 ). Faced with these overwhelming figures, the tendency is to search for technological and IT solutions to manage such data. Yet, unless such genomic data sharing is framed by common policies and the data linked to electronic medical records via harmonized and interoperable systems, it will not improve genomic variant interpretation or inform clinical decisions and targeted health care.
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 | 0.030 | 0.135 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.057 | 0.059 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".