Photobiomodulation reduces drusen volume and improves visual acuity and contrast sensitivity in dry age‐related macular degeneration
Bibliographic record
Abstract
Abstract Purpose To evaluate the efficacy of photobiomodulation ( PBM ) treatment for patients with dry age‐related macular degeneration ( AMD ). Methods Assessments on 42 eyes with dry AMD (age related eye disease study (AREDS) 2–4) were conducted. Multiwavelength light emitting diode ( LED ) light comprising of yellow (590 nm), red (670 nm) and near‐infrared (790 nm) bandwidths was applied to subjects’ eyes for a treatment course of 3 weeks. Outcome measures were changes in best‐corrected visual acuity ( BCVA ), contrast sensitivity ( CS ), drusen volume and central drusen thickness. Results Significant improvement in mean BCVA of 5.90 letters (p < 0.001) was seen on completion of the 3‐week treatment and 5.14 letters (p < 0.001) after 3 months. Contrast sensitivity improved significantly (log unit improvement of 0.11 (p = 0.02) at 3 weeks and 3 months (log unit improvement of 0.16 (p = 0.02) at three cycles per degree. Drusen volume decreased by 0.024 mm 3 (p < 0.001) and central drusen thickness was significantly reduced by a mean of 3.78 μ m (p < 0.001), while overall central retinal thickness and retinal volume remained stable. Conclusion This is the first study demonstrating improvements in functional and anatomical outcomes in dry AMD subjects with PBM therapy. These findings corroborate an earlier pilot study that looked at functional outcome measures. The addition of anatomical evidence contributes to the basis for further development of a non‐invasive PBM treatment for dry AMD .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".