Communicating the Promise for Ocular Gene Therapies: Challenges and Recommendations
Bibliographic record
Abstract
PURPOSE: To identify challenges and pose solutions for communications about ocular gene therapy between patients and clinicians as clinical research progresses. DESIGN: Literature review with recommendations. METHODS: Literature review of science communication best practices to inform recommendations for patient-clinician discussions about ocular gene therapy. RESULTS: Clinicians need to employ communications about ocular gene therapy that are both attentive to patient priorities and concerns and responsive to other sources of information, including overly positive news media and the Internet. Coverage often conflates research with therapy-clinical trials are experimental and are not risk free. If proven safe and efficacious, gene therapy may present a treatment but not a cure for patients who have already experienced vision loss. Clinicians can assist patients by providing realistic estimates for lengthy clinical development timelines and positioning current research within models of clinical translation. This enables patients to weigh future therapeutic options when making current disease management decisions. CONCLUSIONS: Ocular gene therapy clinical trials are raising hopes for treating a myriad of hereditary retinopathies, but most such therapies are many years in the future. Clinicians should be prepared to counter overly positive messaging, found in news media and on the Internet, with optimism tempered by evidence to support the ethical translation of gene therapy and other novel biotherapeutics.
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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 | 0.044 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 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".