Patient preferences in the treatment of diabetic retinopathy.
Bibliographic record
Abstract
OBJECTIVE: Accounting for patient preferences may be especially important in diabetes mellitus, given the challenge in identifying factors associated with treatment adherence. Although preference studies have been performed in diabetes, none have examined treatments used in diabetic retinopathy (DR). The objective of this study was to elicit patient preferences for attributes associated with antivascular endothelial growth factor, focal and panretinal laser, and steroid therapy used in DR management. METHODS: A cross-sectional conjoint survey was administered to DR patients at three Canadian eye centers. The survey involved making tradeoffs among 11 DR treatment attributes, including the chance of improving vision and risks of adverse events over a 1-year treatment period. Attribute utilities were summed for each product profile to determine the most preferred treatment. RESULTS: Based on the results from 161 patients, attributes affecting visual functioning, including improving visual acuity and reducing adverse events (eg, chance of cataracts), were more important than those not directly affecting vision (eg, administration). Overall, 52%, 20%, 17%, and 11% preferred the product profiles matching to the antivascular endothelial growth factor, steroid, focal laser, and panretinal laser therapies. Preferences did not vary substantially by previous treatment experience, age, or type of DR (macular edema, proliferative DR, both or neither), with the exception that more macular edema only patients preferred focal laser over steroid treatment (19% versus 14%, respectively). CONCLUSIONS: When considering the potential effects of treatment over a 1-year period, treatment preferences in DR are most influenced by those that may positively or negatively affect visual functioning.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".