Rett Syndrome: North American Database
Bibliographic record
Abstract
The International Rett Syndrome Association (IRSA) North American database is the first comprehensive compilation of information in the United States and Canada on individuals with Rett syndrome or with another diagnosis in association with MECP2 mutations. The database contains specific information by diagnosis, mutation status, and mutation type and frequency on 1928 participants. Among the 1928 participants, 85.5% were typical, 13.4% were atypical, and 1.1% had MECP2 mutations but did not have Rett syndrome. MECP2 mutations were identified in 914 of 1059 participants (86%): 799 of 870 (92%) participants with typical Rett syndrome had an MECP2 mutation, 94 of 162 (58%) with atypical Rett syndrome had a mutation, and all 21 individuals diagnosed as Not Rett syndrome had a mutation. Missense-type mutations (39.0%) were slightly more common than nonsense type (35.1%). Individual mutation frequency for the 8 common mutations varied from 11.9% for T158M to 4.4% for R106W; large deletions accounted for 6.4% and C-terminal truncations occurred in 8.8%. The remaining mutations (14.3%) occurred singly or in small numbers. This database provides a unique resource for expanding our understanding of Rett syndrome, for comparison with other national databases, and for future study including organization of clinical trials based on the expected emergence of fundamental therapies.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".