Identification of lesion components that influence visual function in age related macular degeneration
Bibliographic record
Abstract
AIMS: To explore the relation between lesion composition as assessed by fundus photography and fluorescein angiography with clinical measures of vision in eyes of patients with age related macular degeneration (AMD). METHODS: A standardised visual function assessment along with colour stereo pair fundus photography was carried out in both eyes of 58 subjects with a confirmed clinical diagnosis of AMD. The size, location, and composition of the macular lesion (blood, exudate, subretinal fluid, pigment, membrane, atrophy, and fibrosis) were measured on the colour photographs using computer assisted image analysis. Of the 58 subjects, 44 also had concurrent fluorescein angiography. Classic and occult choroidal neovascularisation (CNV), blood, blocked fluorescence, fibrosis, geographic atrophy, and the total area of abnormal fluorescence were measured. Multiple linear regression was used to examine the relation between clinical measures of vision and the location and extent of lesion components identified by both colour and fluorescein image capture. RESULTS: The composition of the macular lesion strongly influenced visual function, with atrophy (p=0.001) and fibrosis (p=0.002) accounting for most of the variation. When the location of the lesion with respect to the fovea was examined, fibrosis within the fovea significantly influenced all clinical measures of vision (p=0.008). The regression model selected the total area of abnormal fluorescence and a composite parameter (a semiquantitative measure of the following characteristics: atrophy, exudates, blood, and fibrosis ) from colour photography (r(2) =0.52) as the variables that explained most of the variation in clinical measures of vision. CONCLUSIONS: The composition and extent of the macular lesion strongly influences visual function in eyes with AMD. Both colour photography and angiography yielded information, which together explained considerably more of the variation in the clinical measures of vision than either on its own.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".