Advances in the genetics of eye diseases
Bibliographic record
Abstract
PURPOSE OF REVIEW: An update on heritable eye disease will allow informed patient counseling and improved patient care. RECENT FINDINGS: New loci and genes have been associated with identifiable heritable ocular traits. Molecular genetic analysis is available for many of these genes either as part of research or for clinical testing. The advent of gene array technologies has enabled screening of samples for known mutations in genes linked to various disorders. Exomic sequencing has proven to be particularly successful in research protocols in identifying the genetic causation of rare genetic traits by pooling patient resources and discovering new genes. Further, genetic analysis has led improvement in patient care and counselling, as exemplified by the continued advances in our treatment of retinoblastoma. SUMMARY: Patients and families are commonly eager to participate in either research or clinical testing to improve their understanding of the cause and heritability of an ocular condition. Many patients hope that testing will then lead to appropriate treatments or cures. The success of gene therapy in the RPE65 form of Leber congenital amaurosis has provided a brilliant example of this hope; that a similar trial may become available to other patients and families burdened by genetic disease.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".