Bibliographic record
Abstract
I am grateful for the kind words of Dr. Sadun and colleagues agreeing with the suggestions made in my article with respect to Leber hereditary optic neuropathy (LHON). They make a seminal point with respect to the spread of injury in LHON, and the critical need to understand how it could occur. We are currently attempting to model this mathematically and try to explain why the injury in some optic nerve diseases spreads differently than in others. We look forward to further work in this area. With respect to the role of ATP deficiency vs superoxide signaling, the confusion probably arose because in their 2012 article (1), they explain the preferential involvement of the papillomacular bundle in LHON using the nerve fiber layer stress index, an energy-related measure. Specifically, they wrote: “The NFL-SI equation described by Sadun et al condenses down to the ratio of demand vs supply, …whereby the numerator reflects all the factors that require high-energy supply by the axon and the denominator, the source of that energy.” It was 1 year later, in their excellent 2013 review (2) (not their reference 3) that they include the role of reactive oxygen species signaling. I obviously agree with their more recent article, which matches our hypothesis first stated in 2007 (3). That said, the overwhelming impact of Dr. Sadun and colleagues' contributions in LHON far outweighs any small historical differences, and the most important issue is that we are converging on an exciting mechanism for LHON and possibly other optic neuropathies.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".