Identifying Predictors of Anti-VEGF Treatment Response in Patients with Neovascular Age-Related Macular Degeneration through Discriminant and Principal Component Analysis
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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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".