Information Needs in the Precision Medicine Era: How Genetics Home Reference Can Help
Bibliographic record
Abstract
Precision medicine focuses on understanding individual variability in disease prevention, care, and treatment. The Precision Medicine Initiative, launched by President Obama in early 2015, aims to bring this approach to all areas of health care. However, few consumer-friendly resources exist for the public to learn about precision medicine and the conditions that could be affected by this approach to care. Genetics Home Reference, a website from the US National Library of Medicine, seeks to support precision medicine education by providing the public with summaries of genetic conditions and their associated genes, as well as information about issues related to precision medicine such as disease risk and pharmacogenomics. With the advance of precision medicine, consumer-focused resources like Genetics Home Reference can be foundational in providing context for public understanding of the increasing amount of data that will become available.
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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 | 0.055 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.023 | 0.059 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
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".