IMPACT OF VISUAL IMPAIRMENT ON USE OF CAREGIVING BY INDIVIDUALS WITH AGE-RELATED MACULAR DEGENERATION
Bibliographic record
Abstract
In Brief Background: To assess the patient-reported use of caregiving among individuals with age-related macular degeneration (AMD) and evaluate the impact of visual impairment level on this use. Methods: A survey including the AMD Health and Impact Questionnaire and the Daily Living Tasks Dependent on Vision Questionnaire (DLTV) was mailed to members of the Macular Degeneration Partnership. The study was approved by an institutional review board, and respondents provided consent before participating. Responses were analyzed by estimated visual acuity determined by scores from the DLTV. Deidentified data were analyzed using SAS Version 8.2 (SAS Institute, Cary, NC). Results: Of 803 respondents, 56% were male, and the mean age was 73 years. Use of paid and unpaid help significantly increased as visual acuity decreased. Using a national average for caregiver time, annual costs for caregiving ranged from $225 to $47,086 depending on visual acuity. Conclusion: There are substantial differences in caregiver support with increased AMD severity. Delaying progression of AMD could result in considerable cost savings. This study evaluated patient-reported use of caregiving among individuals with age-related macular degeneration and found that there are substantial differences in the use of caregiver support with increased severity of age-related macular degeneration.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".