Bibliographic record
Abstract
PURPOSE OF REVIEW: There are currently five groups internationally involved in human clinical gene therapy trials for otoferlin-associated hearing loss. This includes (in alphabetical order) the Eye and ENT Hospital Fudan University (China), Lilly-Akouos (USA), Otovia (China), Regeneron (USA), and Sensorion (France). This review summarizes early work that led to these efforts and highlights early published data on clinical outcomes. RECENT FINDINGS: While published outcomes are currently limited, data emerging from each of these clinical trials is highly consistent. Using a dual vector approach to reconstitute full length Otoferlin, all groups report varying degrees of hearing improvement following cochlear gene therapy, with some cases of hearing restoration to normal levels. Recent data suggests that improvement is not limited only to young children but also adolescents and even young adults in some cases. The treatments all appear safe with limited adverse effects associated with the therapies reported. SUMMARY: Gene therapy for otoferlin-related deafness appears highly successful in most cases with limited reported adverse effects or outcomes. This success will undoubtably usher in a new era of gene therapy for other forms of genetic deafness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".