RANIBIZUMAB TREATMENT IN TREATMENT-NAIVE NEOVASCULAR AGE-RELATED MACULAR DEGENERATION
Bibliographic record
Abstract
PURPOSE: To evaluate the effectiveness, safety, and treatment patterns of ranibizumab 0.5 mg in treatment-naive patients with neovascular age-related macular degeneration enrolled in LUMINOUS study. METHODS: This 5-year, prospective, multicenter, observational study recruited 30,138 adult patients (treatment-naive or previously treated with ranibizumab or other ocular treatments) who were treated according to the local ranibizumab label. RESULTS: Six thousand two hundred and forty-one treatment-naive neovascular age-related macular degeneration patients were recruited. Baseline (BL) demographics were, mean (SD) age 75.0 (10.2) years, 54.9% females, and 66.5% Caucasian. The mean (SD) visual acuity (VA; letters) gain at 1 year was 3.1 (16.51) (n = 3,379; BLVA, 51.9 letters [Snellen: 20/92]) with a mean (SD) of 5.0 (2.7) injections and 8.8 (3.3) monitoring visits. Presented by injection frequencies <3 (n = 537), 3 to 6 (n = 1,924), and >6 (n = 918), visual acuity gains were 1.6 (14.93), 3.3 (16.57), and 3.7 (17.21) letters, respectively. Stratified by BLVA <23 (n = 382), 23 to <39 (n = 559), 39 to <60 (n = 929), 60 to <74 (n = 994), and ≥74 (n = 515), visual acuity change was 12.6 (20.63), 6.7 (17.88), 3.6 (16.41), 0.3 (13.83), and -3.0 (11.82) letters, respectively. The incidence of ocular/nonocular adverse events was 8.2%/12.8% and serious adverse events were 0.9%/7.4%, respectively. CONCLUSION: These results demonstrate the effectiveness and safety of ranibizumab in treatment-naive neovascular age-related macular degeneration patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".