Key drivers of visual acuity gains in neovascular age-related macular degeneration in real life: findings from the AURA study
Bibliographic record
Abstract
BACKGROUND/AIMS: To identify predictive markers for the outcomes of anti-vascular endothelial growth factor therapy for neovascular age-related macular degeneration (nAMD). METHODS: AURA was a retrospective, observational, multicentre study that monitored the 2-year outcomes following intravitreal ranibizumab treatment in patients with nAMD. Using stepwise regression analysis, we evaluated the association between visual acuity outcomes, baseline characteristics and resource utilisation in order to determine which variables are significantly linked to outcomes in AURA. We also examined the relationship between visual acuity outcomes and number of injections received. RESULTS: Analyses were performed using data from year 1 (n=1695) and year 2 completers (n=1184). Logistic analysis showed that baseline visual acuity score, age at start of therapy, number of ophthalmoscopies and optical coherence tomography (OCT) (combined) and number of injections (ranibizumab) were significant (p<0.05) prognostic factors for vision maintenance (loss <15 letters) or vision gain (≥15 letters). Patients who received >7 injections (in 1 year) or >14 injections (over 2 years) gained more letters and demonstrated greater vision maintenance (loss of <15 letters) than patients who received fewer injections. There was a significant (p<0.05) association between number of injections and national reimbursement schemes and OCT. CONCLUSIONS: A number of factors that are predictive of treatment outcomes in a real-life setting were identified. Notably, the decline of treatment benefits may be linked to number of injections and a failure to visit clinicians and receive OCT as required. These findings may be helpful in guiding ophthalmologist treatment decisions under limited time and financial constraints. TRIAL REGISTRATION NUMBER: NCT01447043.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".