Multi-country real-life experience of anti-vascular endothelial growth factor therapy for wet age-related macular degeneration
Bibliographic record
Abstract
BACKGROUND/AIMS: Real-life anti-vascular endothelial growth factor (VEGF) therapy use in patients with wet age-related macular degeneration (wAMD) was assessed in a retrospective, observational study in Canada, France, Germany, Ireland, Italy, the Netherlands, UK and Venezuela. METHODS: Medical records of patients with wAMD, who started ranibizumab treatment between 1 January 2009 and 31 August 2009, were evaluated. Data were collected until the end of treatment and/or monitoring or until 31 August 2011. RESULTS: 2227 patients who received ≥1 anti-VEGF injection with a baseline visual acuity assessment and ≥1 postbaseline visual acuity assessment for the treated eye were evaluated. Visual acuity improved until about day 120; thereafter, visual acuity gains were not maintained. Mean change in visual acuity score from baseline to years 1 and 2 was +2.4 and +0.6 letters, respectively. Patients received a mean of 5.0 and 2.2 injections in the first and second year, respectively. There were substantial differences in visual outcomes and injection frequency between countries. More frequent visits and injections were associated with greater improvements in visual acuity. CONCLUSIONS: In clinical practice, fewer injections are administered than in clinical trials. Anti-VEGF treatment resulted in an initial improvement in visual acuity; however, this was not maintained over time. 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".