Genetic testing for retinal dystrophies and dysfunctions: benefits, dilemmas and solutions
Bibliographic record
Abstract
Human retinal dystrophies have unparalleled genetic and clinical diversity and are currently linked to more than 185 genetic loci. Genotyping is a crucial exercise, as human gene-specific clinical trials to study photoreceptor rescue are on their way. Testing confirms the diagnosis at the molecular level and allows for a more precise prognosis of the possible future clinical evolution. As treatments are gene-specific and the 'window of opportunity' is time-sensitive; accurate, rapid and cost-effective genetic testing will play an ever-increasing crucial role. The gold standard is sequencing but is fraught with excessive costs, time, manpower issues and finding non-pathogenic variants. Therefore, no centre offers testing of all currently 132 known genes. Several new micro-array technologies have emerged recently, that offer rapid, cost-effective and accurate genotyping. The new disease chips from Asper Ophthalmics (for Stargardt dystrophy, Leber congenital amaurosis [LCA], Usher syndromes and retinitis pigmentosa) offer an excellent first pass opportunity. All known mutations are placed on the chip and in 4 h a patient's DNA is screened. Identification rates (identifying at least one disease-associated mutation) are currently approximately 70% (Stargardt), approximately 60-70% (LCA) and approximately 45% (Usher syndrome subtype 1). This may be combined with genotype-phenotype correlations that suggest the causal gene from the clinical appearance (e.g. preserved para-arteriolar retinal pigment epithelium suggests the involvement of the CRB1 gene in LCA). As approximately 50% of the retinal dystrophy genes still await discovery, these technologies will improve dramatically as additional novel mutations are added. Genetic testing will then become standard practice to complement the ophthalmic evaluation.
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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 | 0.033 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".