Looking back at genomic medicine in 2011
Bibliographic record
Abstract
Genomic medicine, in its broadest sense of being medical developments informed by ‘omic ’ advances, has continued to move towards the clinic in 2011. To mark the end of the year and the beginning of 2012, the editors of the six sections within Genome Medicine were invited to provide their highlights of the past year and to hint at the developments that we are likely to see in the near future. Six different areas of progress are covered here, but the core of genomic medicine continues to be intrinsically linked to improvements in the underlying technology, and two obvious examples are sequencing and mass spectrometry. Technological advances have enabled larger studies and more complex analyses, allowing researchers and clinicians to track changes within a single cell and yet spot patterns across a whole population and
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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.017 | 0.034 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".