GeneTests-GeneClinics: Genetic testing information for a growing audience
Bibliographic record
Abstract
The development and usage of two companion NIH-funded genetic testing information databases, GeneTests (www.genetests.org) and GeneClinics (www.geneclinics.org), now merged into one web site, reflect the steadily increasing use of genetic testing and the expanding audience for genetic testing information. Established in 1993 as Helix, a genetics laboratory directory of approximately 110 listings, GeneTests has grown into a database of over 900 tests for inherited diseases, a directory of over 500 international laboratories, a directory of over 1,000 U.S. and international genetics clinics, and a resource for educational/teaching materials and reports of summary genetic test data. GeneClinics, founded in 1997 as an expert-authored, peer-reviewed, disease-specific knowledge base relating genetic testing to patient care, has grown steadily, now containing over 130 expert-authored, peer-reviewed full-text entries relating genetic testing information to diagnosis, management, and genetic counseling of specific inherited diseases. In spring 2001 the two databases were merged and in October 2001 the two web sites were merged for the purpose of seamless navigation into the GeneTests-GeneClinics site (www.genetests.org or www.geneclinics.org); the GeneClinics knowledge base was renamed "GeneReviews" to avoid confusion with the U.S. and international clinic directories. As genetic testing has moved steadily out of research venues and into routine medical practice, the user audience for these databases has become international and expansive and includes healthcare providers, patients, educators, policy makers, and the media. The use of these combined resources has grown to approximately 3,200 visits/day.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.168 | 0.155 |
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".