Special Commentary: Early Clinical Development of Cell Replacement Therapy: Considerations for the National Eye Institute Audacious Goals Initiative
Bibliographic record
Abstract
The National Eye Institute launched the Audacious Goals Initiative (AGI) in 2013 with the aim "to restore vision through the regeneration of neurons and neural connections in the eye and visual system." An AGI Town Hall held at the Association for Research in Vision and Ophthalmology Annual Meeting in 2016 brought together basic, translational, and clinical scientists to address the clinical implications of the AGI, with a particular emphasis on diseases amenable to regenerative medicine and strategies to deal with barriers to progess. An example of such a barrier is that replacement of lost neurons may be insufficient because damage to other neurons and non-neuronal cells is common in retinal and optic nerve disease. Reparative processes such as gliosis and fibrosis also can make it difficult to replenish and regenerate neurons. Other issues include choice of animal models, selecting appropriate endpoints, ethics of informed consent, and regulatory issues. Another area critical to next steps in the AGI is the choice of target diseases and the stage at which early development studies should be focused. For example, an advantage of doing clinical trials in patients with early disease is that supporting cellular and structural constituents are still likely to be present. However, regenerative studies in patients with late disease make it easier to detect the effects of replacement therapy against the background of severe visual loss, whereas it may be harder to detect incremental improvement in visual function in those with early disease and considerable remaining visual function. Achieving the goals of the AGI also requires preclinical advances, new imaging techniques, and optimizing translational issues. The work of the AGI is expected to take at least 10 years but should eventually result in therapies to restore some degree of vision to the blind.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.062 | 0.067 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".