Bibliographic record
Abstract
PURPOSE OF REVIEW: Molecular genetics is revolutionizing the diagnosis and treatment of inherited eye diseases. The National Eye Institute of the National Institutes of Health (NIH), in an effort to facilitate future basic and clinical research in inherited eye disease, created The National Ophthalmic Disease Genotyping and Phenotyping Network (eyeGENE). This review describes the process and utility of the eyeGENE program as it relates to ophthalmic clinical practice. RECENT FINDINGS: Over the last few years, genetic testing of specific genes associated with inherited eye conditions is becoming the standard practice. Vision research and human clinical trials relying on molecular genetic testing of individuals with inherited eye conditions are becoming more common. Eye healthcare professionals must consider the options to assist patients in obtaining genetic testing results and locating trials or studies that may have benefit. SUMMARY: eyeGENE is a DNA repository and patient registry for inherited eye diseases coupled to phenotypic descriptors and molecular genetic information. Through eyeGENE, healthcare professionals throughout the United States and Canada can obtain Clinical Laboratory Improvement Amendments-certified clinical molecular genetic results on their patients. Researchers may request access to a de-identified database of phenotype and genotype information about eyeGENE participants and DNA aliquots for their research studies. eyeGENE also offers participants the option of being included in a patient registry, whereby they may be re-contacted if an approved clinical study for which they might qualify is offered.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.193 | 0.219 |
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".