Bibliographic record
Abstract
BACKGROUND: It is of interest to examine paracentral functional deficits in early age-related maculopathy (ARM), as histopathological studies indicate that this is where the earliest changes occur. The purpose of this study is to detect the sensory functional deficits at chosen retinal areas around the fovea and at the fovea itself in patients with early age-related maculopathy and to determine the type of functional losses that are more pronounced in early ARM. METHODS: Ten participants with early ARM and 10 age-matched controls took part. Crowded and uncrowded visual acuity and static and transient contrast sensitivity were measured in the same selected eye of each participant at eight predetermined retinal locations plus the fovea in patients with early ARM and controls. All measurements were made using computer-generated targets. RESULTS: A significant difference between the controls and subjects with ARM was found in low spatial frequency static contrast sensitivity (p = 0.05) but not for transient contrast sensitivity (p = 0.586). Visual acuity (uncrowded VA and crowded VA) showed a borderline difference (p = 0.072 and p = 0.084, respectively). Compared to controls, there was no evidence of increased contour interaction effects in early ARM (p = 0.595). CONCLUSION: The subjects with very early ARM showed significant loss of low spatial frequency static contrast sensitivity before the loss of high contrast VA, indicating that static contrast sensitivity may be one of the earliest functional losses in early ARM and this loss was found to extend across the central 10 degrees of the retina.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".