Identifying Predictors of Anti-VEGF Treatment Response in Patients with Neovascular Age-Related Macular Degeneration through Discriminant and Principal Component Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: AURA was an observational study that monitored visual acuity outcomes following ranibizumab use in neovascular age-related macular degeneration patients over 2 years. The aim of this analysis was to identify factors that were predictive of visual acuity outcomes in AURA. METHODS: The correlation between the baseline characteristics, the use of resources and the visual acuity outcomes in AURA was explored using principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA). The response variables analysed were mean change in visual acuity over 2 years (analysed via PCA) and no decline in visual acuity at 2 years compared with baseline (analysed via PLS-DA). RESULTS: The AURA dataset comprised 2,227 patients and 132 variables. Using PCA and PLS-DA, we found that the number of ranibizumab injections, clinic and monitoring visits, number of optical coherence tomography scans and ophthalmoscopies correlated with a change in visual acuity at Years 1 and 2, and are therefore key drivers of treatment success. CONCLUSION: This is a novel approach to graphically explore relationships between multiple correlated covariates and outcomes in real-life ophthalmology studies. It identified a number of variables that are positively linked with treatment outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it