Progress in treating inherited retinal diseases: Early subretinal gene therapy clinical trials and candidates for future initiatives
Bibliographic record
Abstract
Due to improved phenotyping and genetic characterization, the field of 'incurable' and 'blinding' inherited retinal diseases (IRDs) has moved substantially forward. Decades of ascertainment of IRD patient data from Philadelphia and Toronto centers illustrate the progress from Mendelian genetic types to molecular diagnoses. Molecular genetics have been used not only to clarify diagnoses and to direct counseling but also to enable the first clinical trials of gene-based treatment in these diseases. An overview of the recent reports of gene augmentation clinical trials by subretinal injections is used to reflect on the reasons why there has been limited success in this early venture into therapy. These first-in human experiences have taught that there is a need for advancing the techniques of delivery of the gene products - not only for refining further subretinal trials, but also for evaluating intravitreal delivery. Candidate IRDs for intravitreal gene delivery are then suggested to illustrate some of the disorders that may be amenable to improvement of remaining central vision with the least photoreceptor trauma. A more detailed understanding of the human IRDs to be considered for therapy and the calculated potential for efficacy should be among the routine prerequisites for initiating a clinical trial.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".