Bibliographic record
Abstract
Exponential improvements in genomic technology have allowed researchers focus on the information contained in the human genome, in the hopes of applying that knowledge clinically. The field of genomics, where all of the genes of an individual are considered at once, has already begun to change the way that medicine is practiced. For instance, chromosomal microarrays are already being utilized in the diagnosis of autism spectrum disorder, development delay, intellectual disability and birth defects. By recognizing duplications and deletions which are too small to identify with traditional chromosome analysis, we are able to improve diagnostic yield for these disorders. Whole genome sequencing has been used to diagnosis genetic illnesses, even in cases when the clinical picture or diagnosis is unclear. Through pharmacogenomics, which can help explain how genetic variants affect drug metabolism, we will be able to decrease the staggering incidence of adverse drug reactions, as well as to guide physicians on which medications are the most appropriate for individual patients. With better understanding of how genomic changes lead to illness, the body’s response to illness and treatment, physicians will be able to practice more personalized medicine, offering more effective and safer treatment. Genomics has already begun to impact medical care and will likely revolutionize how medicine is practiced in the near future.
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 | 0.037 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".