Long term outcomes in dry age-related macular degeneration following low vision rehabilitation interventions
Bibliographic record
Abstract
BACKGROUND: Age-related macular degeneration (AMD) is the leading cause of loss of vision in the older age groups. In the absence of a known therapy, low vision rehabilitation aims at preserving residual functional vision at optimal levels. Long term functional outcomes from Low Vision Rehabilitation (LVR) in AMD cases were never scrutinized in the past. This study brings some clarification in this matter. METHODS: This is a retrospective case series study including data up to 2 years following the baseline visit. Low Vision Assessments included microperimetry testing and recommendations for low vision devices for distance vision. Outcomes measures selected for this study were best corrected distance visual acuity, fixation stability and preferred retinal locus (PRL) topography and LVR interventions. RESULTS: Data on 17 patients with an average age of 89.2 ± 4.4 years was collected. In those with better vision than 20/400 loss of vision was about 1.4 letter per year as tested with ETDRS charts compared with losses of four letters per year in a population without LVR interventions. Fixation stability continued to deteriorate while PRL eccentricity seemed to remain the same. In about half of cases there was a change in the topographic location of the PRL to a different retinal quadrant. CONCLUSION: Long term, as expected, changes were noticed in visual acuity, fixation stability and PRL topography. However, it seems that LVR interventions for distance vision help patients retain significantly better functional vision at the 2 years follow up interval when compared to others.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".