Choroideremia research: Report and perspectives on the second international scientific symposium for choroideremia
Bibliographic record
Abstract
PURPOSE: To discuss progress in research on choroideremia (CHM) and related retinopathies with special emphasis on gene therapy approaches. METHODS: Biomedical and clinical researchers from across the world as well as representatives of the social science research community were convened to the 2nd International Scientific Symposium for Choroideremia in Denver, Colorado in June 2014 to enhance our understanding of CHM and accelerate the translation of research to clinical application for the benefit of those affected by CHM. RESULTS: Pre-clinical research using cell and animal models continues to further our understanding in the pathogenesis of CHM as well as to demonstrate proof-of-concept for gene transfer strategies. With the advent of modern imaging technology, better outcome measures are being defined for upcoming clinical trials. Results from the first gene therapy trial in CHM show promise, with sustained visual improvement over 6 months post-treatment. Current and next-generation gene transfer approaches may make targeted vector delivery possible in the future for CHM and other inherited retinal diseases. CONCLUSIONS: While no accepted therapies exist for CHM, promising approaches using viral-vectored gene therapy and cell therapies are entering clinical trials for eye diseases, with gene therapy trials underway for CHM.
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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 | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".