Photobiomodulation in dry age‐related macular degeneration
Bibliographic record
Abstract
We read with great interest the article by Merry et al. (2016) on photobiomodulation (PBM) in dry age-related macular degeneration (AMD). The tremendous results of PBM in the improvement of best-corrected visual acuity and contrast sensitivity notwithstanding, there were certain misgivings in their study which they succinctly highlighted as well. A previous similar study (TORPA) by the same authors found no difference in the preferential retinal locus (PRL) in 18 subjects treated with PBM on microperimetry (Merry et al. 2012, ARVO abstract). Preferential retinal locus (PRL) is a surrogate marker for improved and steady fixation. Microperimetry was not employed in this follow-up study. It would be interesting to see the subjective change in PRL and improvement in microperimetry in the subjects using PBM. Fundus autofluorescence (FAF), central retinal thickness (CRT) and retinal volume (RV) used as anatomical adjuncts for the change in dry AMD with PBM showed no significant improvement and in fact showed mild worsening of FAF at visit 2 (3 months). While drusen volume showed a significant change for the better, it seems odd that the FAF, which is a surrogate for retinal pigment epithelium function, showed worsening in area. The authors could try to explain this paradox. Despite these limitations, we are glad to see a novel therapeutic modality has a significant impact in dry AMD which offers more hope to AMD patients. We congratulate the authors for the outstanding results and hope to see a dose ranging of PBM would help identify the beneficial effect of specific wavelengths in 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".