Patient preferences in retinal drug delivery
Bibliographic record
Abstract
Retinal vascular diseases (RVDs) are often treated with intravitreally (IVT) injected drugs, with relatively low patient compliance and potential risks. Ongoing research explores alternative RVD treatments, including eye drops and oral tablets. This study surveyed RVD patients treated with IVT injections to establish factors influencing low compliance rates while gauging treatment delivery method preferences. Demographics, perspectives, and treatment preferences were collected via IRB-approved, self-administered survey sent to Glick Eye Institute patients treated via IVT injections. Demographics, diagnoses, and treatments were ascertained from respondents' medical records. Gender, age, and number of IVT injections received were used as stratifications. Five-level Likert-style scales and t-tests evaluated responses and stratification comparisons. The most common diagnoses in the respondent population (n = 54; response rate = 5%) were age-related macular degeneration, macular edema, and diabetic retinopathy. Respondents had varying levels of education, income, and age. Most (83%) admitted feeling anxious prior to their first IVT injection, but 80% reported willingness to receive IVT injections indefinitely, with a preference for ophthalmologist visits every 1-3 months. Eye drops would be preferred over IVT injections by 76% of respondents, while 65% preferred oral tablets, due to several perceived negative factors of IVT injections and positive factors for eye drops. Stratified groups did not differ in responses to survey questions. RVD patients will accept IVT injections for vision preservation, but alternative delivery methods like eye drops or oral tablets would be preferred. Thus, development of eye drop and oral therapeutics for RVD treatment is further emphasized by these findings.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".