The Retinal Ganglion Cell Repopulation, Stem Cell Transplantation, and Optic Nerve Regeneration Consortium
Bibliographic record
Abstract
Purpose: The Retinal Ganglion Cell (RGC) Repopulation, Stem Cell Transplantation, and Optic Nerve Regeneration (RReSTORe) consortium was founded in 2021 to help address the numerous scientific and clinical obstacles that impede development of vision-restorative treatments for patients with optic neuropathies. The goals of the RReSTORe consortium are: (1) to define and prioritize the most critical challenges and questions related to RGC regeneration; (2) to brainstorm innovative tools and experimental approaches to meet these challenges; and (3) to foster opportunities for collaborative scientific research among diverse investigators. Design and Participants: The RReSTORe consortium currently includes > 220 members spanning all career stages worldwide and is directed by an organizing committee comprised of 15 leading scientists and physician-scientists of diverse backgrounds. Methods: Herein, we describe the structure and organization of the RReSTORe consortium, its activities to date, and the perceived impact that the consortium has had on the field based on a survey of participants. Results: In addition to helping propel the field of regenerative medicine as applied to optic neuropathies, the RReSTORe consortium serves as a framework for developing large collaborative groups aimed at tackling audacious goals that may be expanded beyond ophthalmology and vision science. Conclusions: The development of innovative interventions capable of restoring vision for patients suffering from optic neuropathy would be transformative for the ophthalmology field, and may set the stage for functional restoration in other central nervous system disorders. By coordinating large-scale, international collaborations among scientists with diverse and complementary expertise, we are confident that the RReSTORe consortium will help to accelerate the field toward clinical translation. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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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 | 0.031 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".