PIGMENT EPITHELIAL DETACHMENT THICKNESS AND VARIABILITY AFFECTS VISUAL OUTCOMES IN PATIENTS WITH NEOVASCULAR AGE-RELATED MACULAR DEGENERATION
Bibliographic record
Abstract
PURPOSE: To evaluate the impact of pigment epithelial detachment (PED) thickness (i.e., height) and thickness variability on best-corrected visual acuity outcomes in patients with neovascular age-related macular degeneration in the Phase 3 HAWK and HARRIER trials. METHODS: Optical coherence tomography images from the pooled brolucizumab 6 mg and aflibercept 2 mg arms were analyzed for the maximum PED thickness across the macula at baseline through to week 96. Best-corrected visual acuity outcomes were compared in patients with different PED thickness and variability cut-off thresholds. RESULTS: Greater PED thickness at baseline or at week 12 was associated with lower mean best-corrected visual acuity gain from baseline to week 96 (baseline PED ≥200 µ m: +4.6 letters; <200 µ m: +7.0 letters; week 12 PED ≥100 µ m: +5.6 letters; <100 µ m: +6.6 letters). Eyes with the largest PED thickness variability from week 12 through week 96 gained fewer letters from baseline at week 96 (≥33 µ m: +3.3 letters; <9 µ m: +6.2 letters). Furthermore, increased PED thickness at week 48 was associated with higher prevalence of intraretinal and subretinal fluid. CONCLUSION: In this treatment-agnostic analysis, greater PED thickness and PED thickness variability were associated with poorer visual outcomes in patients with neovascular age-related macular degeneration and greater neovascular activity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".