Developing Cellular Therapies for Retinal Degenerative Diseases
Bibliographic record
Abstract
Biomedical advances in vision research have been greatly facilitated by the clinical accessibility of the visual system, its ease of experimental manipulation, and its ability to be functionally monitored in real time with noninvasive imaging techniques at the level of single cells and with quantitative end-point measures. A recent example is the development of stem cell-based therapies for degenerative eye diseases including AMD. Two phase I clinical trials using embryonic stem cell-derived RPE are already underway and several others using both pluripotent and multipotent adult stem cells are in earlier stages of development. These clinical trials will use a variety of cell types, including embryonic or induced pluripotent stem cell-derived RPE, bone marrow- or umbilical cord-derived mesenchymal stem cells, fetal neural or retinal progenitor cells, and adult RPE stem cells-derived RPE. Although quite distinct, these approaches, share common principles, concerns and issues across the clinical development pipeline. These considerations were a central part of the discussions at a recent National Eye Institute meeting on the development of cellular therapies for retinal degenerative disease. At this meeting, emphasis was placed on the general value of identifying and sharing information in the so-called "precompetitive space." The utility of this behavior was described in terms of how it could allow us to remove road blocks in the clinical development pipeline, and more efficiently and economically move stem cell-based therapies for retinal degenerative diseases toward the clinic. Many of the ocular stem cell approaches we discuss are also being used more broadly, for nonocular conditions and therefore the model we develop here, using the precompetitive space, should benefit the entire scientific community.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".