The role of full-field stimulus threshold in evaluating Bietti crystalline dystrophy
Bibliographic record
Abstract
PURPOSE: Visual functional testing methods are limited and cannot be well applied in all kinds of ocular diseases, especially those with low-vision. To determine whether full-field stimulus threshold (FST) is an effective method in evaluating Bietti crystalline dystrophy (BCD), with the capability in replacing other traditional visual functional methods, we designed the study. METHODS: This study was a prospective cross-sectional observational study, conducted from September 2022 to March 2023. It was a sub-study of Beijing Tongren Eye Disease Clinical Database Biobank. The study was conducted in Beijing Tongren Hospital, Capital Medical University. BCD patients who were diagnosed based on clinical features, validated by whole-exome sequencing were included. The normal volunteers were included as control group. All BCD patients underwent comprehensive evaluations, including FST, best-corrected visual acuity (BCVA), color vision, microperimetry, full-field electroretinography (ffERG), optical coherence tomography (OCT), multimodal imaging system and 4 self-reported questionnaires. The normal control group subjects only conducted FST examination. The correlation between FST and BCVA, and the differences of FST values between disease severity groups, were evaluated. The BCVA logistic regression model, FST model and hybrid model's performance (area under the curve [AUC]) were tested in predicting disease severity. RESULTS: 43 BCD patients (40.60 ± 8.57 years, 22 female [51%]) and 36 normal volunteers (38.56 ± 12.42 years, 21 female [58%]) were included. FST showed a moderate correlation with BCVA scores. Stage 2 group exhibited notably lower FST values than the stage 3 group, while the severely diminished group showed remarkable lower FST values than the extinguished group. The hybrid model showed better performance (AUC = 0.9221 and 0.9496) than FST model (AUC = 0.9026 and 0.9429), better than BCVA model (AUC = 0.8549 and 0.8487) in predicting disease severity. CONCLUSIONS: The FST serves as a useful indicator for evaluating visual function and predicting the severity of disease in patients with BCD. The clinical implementation of FST value will serve as an important assay in various ocular diseases, necessitating further validation studies prior to its application.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".