Stargardt disease – what general ophthalmologists should know about this macular dystrophy
Bibliographic record
Abstract
On the basis of the available literature in the PubMed database, we describe the pathogenesis, diagnosis and therapeutic trials in Stargardt disease (STGD).STGD is the most commonly inherited cause of visual loss in childhood and adulthood.STGD is mostly inherited in an autosomal recessive pattern and is most commonly caused by mutations in the ABCA4 gene leading to accumulation of the lipofuscin-like substance A2E, toxic to retinal pigment epithelium (RPE) and photoreceptors.Genetic analysis is necessary for a reliable diagnosis.STGD can be classified into 3 types with different prognoses based on the flash electroretinogram (ERG) and fundus autofluorescence (FAF).Spectral domain optical coherence tomography (SD-OCT), multifocal ERG (mfERG) and pattern ERG (PERG) are useful in the early detection of changes.Adaptive optics scanning laser ophthalmoscopy (AOSLO) may be used to evaluate progression and for selection of patients for clinical trials.At present, there are clinical trials: gene replacement therapy (Star-Gen), subretinal injection of RPE cells, oral substitutes reducing the accumulation of A2E (Alkeus, Acucella), and intravitreal injection of a drug that inhibits the complement system (Zimura).Significant progress is observed in identifying this condition at an early stage, determining clinical features, prognosis, molecular diagnostics and in understanding the pathogenesis of this disease.We are awaiting long-term results of the 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.025 |
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".