RettBASE: The IRSA MECP2 variation database—a new mutation database in evolution
Bibliographic record
Abstract
Rett syndrome (RTT) is a neurodevelopmental disorder affecting primarily females, with an incidence of around 1 in 15,000 females. In 1999, mutations in the X-linked gene methyl-CpG-binding protein 2 (MECP2) were first reported in RTT subjects, and since that time there have been a number of publications describing cohorts of patients and their mutations. In addition, MECP2 mutations have been reported in patients who do not fit the diagnostic criteria for Rett syndrome. We have developed a new locus-specific database, RettBASE (http://mecp2.chw.edu.au/), loosely based on the PAHdb website. The aim is to obtain data relating to all known instances of MECP2 variations, including published ta and data directly submitted by one of various means (either by using an online submission form, or by sending the same form in Adobe portable document format (pdf) or Microsoft Word format by email or fax to the database curators). The database has a range of query capabilities, allowing for simple or complex interrogation of the database. To address the issue of patient confidentiality, we have incorporated an Excel spreadsheet algorithm that allows the generation of a unique number based on the subject's name and date of birth. We believe this database will prove to be a useful resource, allowing the development of accurate prevalence data for disease-causing mutations, providing a catalog of polymorphisms, and potentially allowing more accurate phenotype-genotype correlations to be drawn.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".