A.05 An epidemiologic study of SLC52A2-related Riboflavin Transport Deficiency
Bibliographic record
Abstract
Background: Riboflavin transporter deficiency (RTD), formerly known as Brown-Vialetto-van Laere syndrome, is an early-onset neurodegenerative disorder with distinctive phenotypes. RTD is caused by mutations in either the SLC52A2 or SLC52A3 genes that encode riboflavin transporters RFVT-2 and RFVT-3, respectively. Methods: This was a 3-year retrospective case review from the Cure RTD International Registry. Results: 73 individuals (~60% female, 14 deceased) from 56 families had genetically confirmed RTD Type 2, including 30 novel SLC52A2 mutations (24 missense, 2 nonsense, 4 deletion). The mean ages at symptom onset and at diagnosis were 2.4 years (SD 1.5, range 0.25–8, n=63) and 12.0 years (SD 10.2, range 0.75–52, n=56) respectively. Most common presenting symptoms were sensory ataxia (n=43), sensorineural hearing loss (n=22), nystagmus/visual loss secondary to optic atrophy (n=14), upper limb weakness (n=11), and respiratory insufficiency (n=9). Treatment included high dose riboflavin, other supplements, and supportive care; 7 individuals required transfusions for anemia pre-riboflavin treatment and 17 (25%) received a cochlear implant. The minimum prevalence of RTD was estimated to be 1 per million, with >100 new cases each year. Conclusions: This is the largest case series of RTD to date. Early recognition and prompt riboflavin treatment is essential for survival and optimal outcome.
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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 | 0.001 | 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) | 0.005 | 0.001 |
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".