Outcomes of 16-week extension of anti-VEGF therapy in neovascular age-related macular degeneration
Bibliographic record
Abstract
OBJECTIVE: To determine the outcomes of extending anti-VEGF injection intervals to 4 months in neovascular age-related macular degeneration (nAMD). DESIGN: A prospective cohort study. PARTICIPANTS: Patients undergoing injections with standard-dose anti-VEGF (aflibercept, ranibizumab) with documented disease stability at 3-month injection intervals for ≥2 years. METHODS: The injection interval was extended to 4 months. The primary outcome of disease stability was defined as no clinical evidence of lesion growth, blood, or intraretinal or new subretinal fluid seen on ocular coherence tomography (OCT). Demographic data, visual acuity, exam findings, and OCT data were collected. RESULTS: This study included 88 eyes (83.4 ± 7.3 years, 64.8% female) with nAMD extended to injection intervals of 4 months (56 eyes with aflibercept and 32 with ranibizumab). The recurrence rate was 10.2% (9/88). Four eyes recurred after the first 4-month extension interval, 2 eyes at the 8-month follow-up, 2 eyes at 16 months, and 1 eye at 22 months. In eyes with a recurrence (n = 9), there was no significant difference (p > .05) between mean visual acuity prior to recurrence (0.18 ± 0.13 [20/30]) and at final follow-up postrecurrence (0.21 ± 0.17 [20/30]). All but 1 case returned to within 1 Snellen line of visual acuity at final follow-up. All eyes were able to regain stability at 3- or 4-month injection intervals. CONCLUSIONS: In nAMD patients with disease stability at 3-month injection intervals for at least 2 years, the majority remained stable when extended to 4 months. Recurrences were able to achieve stability again with shorter injection intervals, without a persistent decline in visual acuity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".