Editorial: Hereditary Optic Neuropathies: A New Perspective
Bibliographic record
Abstract
Over 30 years have elapsed from the landmark finding of the first point mutation in mitochondrial DNA (mtDNA) associated with Leber's hereditary optic neuropathy (LHON) by Doug Wallace and his team in 1988. 1 Twelve years later came the identification of the nuclear OPA1 gene, which encodes for a mitochondrial dynamin-like protein, whose pathogenic mutations cause Dominant Optic Atrophy (DOA). 2,3 This was a predictable convergence, as both nuclear and mtDNA mutations may cause a similar mitochondrial disorder resulting in vision loss, identifying DOA as a mitochondrial disease similar to LHON. 4,5 We are delighted to have compiled this special issue of Overall, the field of hereditary optic neuropathies remains extremely dynamic and lively. By presenting the "tip of the neurodegeneration iceberg" in this special issue we hope to demonstrate the importance of these diseases as an informative model to make key observation of relevance to other neurodegenerative disorders. 9 From the genetic foundation, to the clinical and preclinical research, and ultimately ending with new therapeutic strategies, we look forward to further groundbreaking progress in hereditary optic neuropathies, leading to the ultimate goal of an effective cure for these patients, preserving vision.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".