Macular spatial distribution of preserved autofluorescence in patients with choroideremia
Bibliographic record
Abstract
BACKGROUND/AIMS: To better understand the pattern of degeneration progression in cases with choroideremia. METHODS: A cohort of genotypically confirmed choroideremia cases who underwent optical coherence tomography (OCT) and fundus autofluorescence (FAF) imaging was studied. Using HEYEX review software, the foveal centre was marked on FAF images under guidance of corresponding OCT images, followed by application of an ETDRS grid. The boundaries of preserved autofluorescence (AF) were manually segmented in each individual ETDRS subfield. The regional distribution of preserved AF was assessed by comparing its area among the various subfields. RESULTS: A total of 168 eyes from 84 choroideremia cases were enrolled. There was a statistically significant difference in the amount of preserved AF area between inner subfields as determined by one-way analysis of variance (F (3,668)=9.997, p<0.001) and also between outer subfields (F (3,668)=8.348, p<0.001). A Tukey posthoc test revealed that the preserved AF area in the nasal subfields in both the inner and outer subfields was significantly smaller compared with analogue subfields. CONCLUSION: The asymmetric spatial distribution of preserved AF in choroideremia (corresponding to the stellate shaped nature of these regions) suggests that the progression of degeneration has directional preference.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".