A randomised trial of bevacizumab and reduced light dose photodynamic therapy in age-related macular degeneration: the VIA study
Bibliographic record
Abstract
AIM: To determine if reduced light-dose photodynamic therapy (PDT) combined with bevacizumab will decrease the number of bevacizumab treatments required over 6 months compared with bevacizumab monotherapy in neovascular age-related macular degeneration (AMD). METHODS: Thirty-six patients with neovascular AMD were recruited for this randomised, double-masked, controlled clinical trial. Patients received intravitreal bevacizumab plus PDT using a light dose of either 25 J/cm2 (group 1) or 12 J/cm2 (group 2), or intravitreal bevacizumab plus sham PDT (group 3). Patients returned monthly for possible retreatment with bevacizumab or combination therapy (with a 3-month minimum interval between combination treatments); retreatment decisions were primarily based on optical coherence tomography. The main outcome measure was the mean number of bevacizumab treatments required over 6 months. RESULTS: Patients required a mean of 2.8 bevacizumab treatments in group 1 and 2.5 in group 2, compared with 5.1 in group 3 (p = 0.005 and p<0.001, respectively). CONCLUSIONS: Combination bevacizumab and 25 J/cm2 or 12 J/cm2 PDT significantly reduced the number of bevacizumab treatments required over 6 months. This study was powered to examine number of treatments, but not visual acuities. Nevertheless, visual acuities responded favourably in all three groups. Further studies will be helpful to explore visual outcomes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".