Microperimetry as an Outcome Measure in Choroideremia Trials: Reproducibility and Beyond
Bibliographic record
Abstract
PURPOSE: To determine test-retest repeatability of microperimetry testing (MP) in choroideremia (CHM) subjects using standard and personalized stimulus grids. METHODS: Fifteen CHM subjects (28 eyes) underwent consecutive repeat examinations with the Macular Integrity Assessment (MAIA) microperimeter using a standard (10°) and a customized macular grid adapted to individual macular pathology. Repeatability of standard-grid mean (MS) and point-wise (PWS) sensitivity was determined and compared with age-matched controls (seven eyes), with PWS separately analyzed for loci within and outside the border of degeneration. Interpolated volumetric indices were used to estimate repeatability of customized grids and compare their performance to standard grids. RESULTS: Test-retest measures of standard-grid MS yielded higher coefficients of variation (CV) in CHM subjects compared with controls (0.09 vs. 0.02). Volumetric indices from customized grids improved repeatability by driving CV values to 0.05 and close to 0.02 for region-of-interest (ROI) analysis. Variability of PWS was significantly higher in CHM, especially at the border of degeneration (10.68 vs. 4.74 dB at the central retina, P < 0.001). CONCLUSIONS: Microperimetry testing in CHM shows high test-retest variation at the border of degeneration, which influences repeatability of MS measures. Volumetric measures from customized grids can improve reliability of both global and regional sensitivity assessment. Nevertheless, inherent test-retest variation of individual points needs to be taken into account when assessing potential functional decline and/or disease progression.
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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 | 0.038 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".