Impact of visual impairment on service and device use by individuals with age-related macular degeneration (AMD)
Bibliographic record
Abstract
PURPOSE: To assess the patient-reported use of services, supplements, and devices among individuals with age-related macular degeneration (AMD) and evaluate the impact of visual impairment level on this use. METHOD: Data for this study were collected using two instruments, the AMD Health and Impact Questionnaire and the Daily Living Tasks Dependent on Vision questionnaire (DLTV). Both questionnaires were mailed to members of the Macular Degeneration Partnership. The study was approved by an IRB and respondents provided consent before participating. Respondents' visual acuity (VA) was estimated using scores from the DLTV, while use of services and devices was collected from the AMD Questionnaire. De-identified data were analysed in SAS. RESULTS: Of 803 respondents, 56% were male and the mean age was 73 years. Use of services (e.g., counseling, rehabilitation), and devices significantly increased as VA decreased. Using standard US costs, costs for services, supplements, and devices ranged from 506-1619 US dollars depending on VA. CONCLUSION: There are substantial differences in service and device use with increased AMD severity. Delaying progression of AMD could result in considerable cost savings.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".