Differential Macular Morphology in Patients with<i>RPE65</i>-,<i>CEP290</i>-,<i>GUCY2D</i>-, and<i>AIPL1</i>-Related Leber Congenital Amaurosis
Bibliographic record
Abstract
PURPOSE: To evaluate genotypic and macular morphologic correlations in patients with RPE65-, CEP290-, GUCY2D-, or AIPL1-related Leber congenital amaurosis (LCA) using spectral-domain optical coherence tomography (SD-OCT). METHODS: SD-OCT macular scans were performed in 21 patients, including 10 with RPE65, 7 with CEP290, 3 with GUCY2D, and 1 with AIPL1 mutations. An image processing software was used to manually draw segmentation lines by three observers. Lamellar structure was evaluated based on the number of retinal layers on segmented images. Total retinal thickness was measured at the central macular and perifoveal areas by using an automated algorithm. RESULTS: All three patients with GUCY2D mutations (age range, 20-53 years) retained six retinal layers with visible photoreceptor inner/outer segment juncture (PSJ). However, the preservation of lamellar structures did not parallel better visual acuity. Patients with other mutations had poorly defined PSJ and disorganized retinal lamellar structures, where only one to three retinal layers could be observed. Patients with CEP290 mutations trended to have retention of the outer nuclear layer at the fovea and macular thickening, especially at younger ages. In patients with RPE65 (age range, 20-71 years) and AIPL1 mutations (age, 22 years), macular thickness was markedly decreased. Disorganization of retinal lamellar structures in the RPE65 group trended toward a worsening with increasing age. CONCLUSIONS: Variations of macular microstructures were observed among LCA patients with different genotypes. Disorganization of retinal lamellar structure was generally age related. Preservation of retinal microanatomic structures may not be associated with better visual acuity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".