Retrospective Analysis of Structural Disease Progression in Retinitis Pigmentosa Utilizing Multimodal Imaging
Bibliographic record
Abstract
In this report, we assess the natural progression rate of retinitis pigmentosa (RP) over an average of three years using spectral-domain optical coherence tomography (SD-OCT) and short wavelength fundus autofluorescence (SW-AF). Measurement of the ellipsoid zone (EZ) line width and hyperautofluorescent ring diameters was performed in 81 patients with RP in a retrospective, longitudinal fashion. Rate of structural disease progression, symmetry between eyes, and test-retest variability were quantified. We observed on average, EZ-line widths decreased by 140 µm (5.2%, p < 0.001) per year, and average horizontal and vertical hyperautofluorescent ring diameters decreased by 149 µm (3.6%, p < 0.001) and 120 µm (3.9%, p < 0.001) per year, respectively. The 95th percentile of this cohort had differences in progression slopes between eyes that were less than 154 µm, 118 µm, and 132 µm for EZ-line width and horizontal and vertical ring diameters, respectively. For all measures except horizontal ring diameter, progression rates were significantly slower at end-stage disease. From our data, we observed a statistically significant progression rate in EZ line width and SW-AF ring diameters over time, verifying the utility of these measurements for disease monitoring purposes. Additionally, calculated differences in progression slopes between eyes may prove useful for investigators evaluating the efficacy of unilateral treatments for RP in clinical trials.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".