Summary of findings table: photocoagulation of drusen vs control: Loss of 0.3 log units or more of contrast sensitivity
Summary of findings table: photocoagulation of drusen showed no clear difference in loss of ≥ 0.3 log units of contrast sensitivity compared with control (low certainty).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Summary of findings table: photocoagulation of drusen vs control Loss of ≥ 0.3 log units of contrast sensitivity Summary of findings table: photocoagulation of drusen showed no clear difference in loss of ≥ 0.3 log units of contrast sensitivity compared with control (low certainty). Laser treatment of drusen to prevent progression to advanced age‐related macular degeneration (2015) · Cochrane Headline outcomes only | OR 0.82 [0.2, 3.31] | 1 study · 82 participants | Low |
Summary of findings table: photocoagulation of drusen vs control Loss of 0.3 log units or more of contrast sensitivity Summary of findings table: photocoagulation of drusen showed no clear difference in loss of 0.3 log units or more of contrast sensitivity compared with control (low certainty). Laser treatment of drusen to prevent progression to advanced age‐related macular degeneration (2009) · Cochrane Headline outcomes onlyearlier version CD006537.PUB2, superseded | OR 0.82 [0.2, 3.31] | 1 study · 82 participants | Low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Laser treatment of drusen to prevent progression to advanced age‐related macular degeneration 2015 · Cochrane Headline outcomes only | Not reported | list not extracted yet | Not assessed |
Studies behind the findings
None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.