Long-Term Efficacy of a Treat-and-Extend Regimen with Ranibizumab in Patients with Neovascular Age-Related Macular Disease: An Open-Label 12-Month Extension to the CANTREAT Study
Bibliographic record
Abstract
INTRODUCTION: The objective of this study is to assess the long-term effectiveness of a treat-and-extend (T&E) anti-vascular endothelial growth factor regimen in patients with neovascular age-related macular degeneration who remain on T&E and those switched from once-monthly (OM) dosing to T&E (OM-T&E). METHODS: In this 12-month extension of the 2-year CANTREAT study, patients received intravitreal ranibizumab 0.5 mg in a T&E regimen. Main outcome measures included mean change in best-corrected visual acuity (BCVA) from baseline and from month 24 to month 36; percentages of patients who gained ≥5, ≥10, or ≥15 Early Treatment of Diabetic Retinopathy Study (ETDRS) letters or lost ≥5, ≥10, or ≥15 letters from baseline and from month 24 to month 36; and number of injections administered from baseline and from month 24 to month 36 for both groups. RESULTS: Of the 139 patients (73 T&E, 66 OM-T&E) in the extension, 121 (68 T&E, 53 OM-T&E) completed 36 months. Mean (standard deviation [SD]) BCVA changes from baseline to the extension last visit (month 33-36) were +6.6 (11.4) letters in the T&E group and +4.8 (14.3) letters in the OM-T&E group, representing maintenance of 24-month gains. The mean (SD) numbers of injections during the extension were 7.3 (2.7) for T&E and 7.1 (2.8) for OM-T&E. DISCUSSION/CONCLUSION: These findings suggest that after 36 months of treatment, the mean BCVA improvement achieved at 24 months is maintained for both the patients exclusively treated with the T&E regimen and those that switched to T&E after 24 months in the OM regimen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".