Benchmarking test-retest variability in microperimetry for intermediate age-related macular degeneration using MP-3 and MAIA
Bibliographic record
Abstract
OBJECTIVE: Microperimetry (MP) has emerged as a clinical functional endpoint in nonexudative age-related macular degeneration (AMD). In this study, we aim to provide reference values for test-retest outcomes on two MP devices in intermediate AMD (iAMD). DESIGN: Prospective, cross-sectional study. PARTICIPANTS: 3 600 stimuli from 20 eyes in 20 subjects. METHODS: Patients diagnosed with iAMD underwent consecutive testing on MP-3 (NIKED, Gamagori, Japan) and MAIA (CenterVue Icare, Padova, Italy). The obtained point-wise sensitivity (PWS) measurements were superimposed with optical coherence tomography (OCT) (Spectralis, Heildelberg Engineering) acquired. Hyperreflective foci (HRF), drusen volume, ellipsoid zone (EZ)-thickness and outer nuclear layer (ONL)-thickness were quantified with deep-learning algorithms. Subretinal drusenoid deposits (SDD) were manually annotated. We assessed test-retest repeatability at the location of these biomarkers using Bland-Altmann coefficients of repeatability. Furthermore, interdevice correlation, fixation stabilities, and examination durations were evaluated. RESULTS: Comparable overall point-wise retest variances were detected for MP-3 (±4.54 dB) and MAIA (±5.24 dB). SDDs led to significantly worse repeatability in the MAIA device (p = 0.03). Drusen, HRF, EZ-thickness, and ONL thickness had no significant impact on test-retest variance. A good intradevice correlation (MP-3: 0.869 [0.851 - 0.886] MAIA 0.848 [0.827 - 0.867]), and a good mean interdevice correlation (0.841 [0.819 - 0.861]) was observed. CONCLUSIONS: Intradevice and interdevice repeatability for MP examinations with MP-3 and MAIA in patients with iAMD can be considered as good. Biomarkers except for SDD show no significant impact in repeatability in both devices. This supports MP as a reliable functional endpoint in clinical trials in iAMD.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".