Ranibizumab in Diabetic Macular Oedema – A Benefit–risk Analysis of Ranibizumab 0.5 mg PRN Versus Laser Treatment
Bibliographic record
Abstract
INTRODUCTION: The structured Benefit-risk Action Team (BRAT) approach aims to assist healthcare decision makers in treatment assessments. We applied BRAT to compare the benefit-risk profile of ranibizumab 0.5 mg versus laser photocoagulation for the treatment of diabetic macular oedema (DMO). METHODS: One-year data for the ranibizumab 0.5 mg pro re nata (PRN) and laser arms of the phase III trials RESPOND (NCT01135914; n=220), RESTORE (NCT00687804; n=345), and REVEAL (NCT00989989; n=396) were included in the analysis. The benefit measures included ≥10 letters gain/avoidance of loss in best-corrected visual acuity (BCVA), achieving central retinal thickness (CRT) <275 μm, and 25-item Visual Function Questionnaire (VFQ-25) outcomes. The risks measures included endophthalmitis, intraocular pressure increase, hypertension, proteinuria, arterial/venous thromboembolic events and deaths. RESULTS: Ranibizumab treatment provided significant benefits compared with laser for ≥10 letter BCVA gain at month 12 (387/1,000 versus 152/1,000 patients), CRT <275 μm at 12 months (474/1,000 versus 348/1,000 patients), and improvement of ≥6.06 on the VFQ-25 near activities subscale (325/1,000 versus 245/1,000 patients). Results for the risk measures were similar for both treatments. CONCLUSIONS: Superior clinically relevant outcomes were observed with ranibizumab 0.5 mg PRN compared with laser without compromising on safety. This analysis further supports the positive benefit-risk profile of ranibizumab 0.5 mg PRN.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".