Genotype-phenotype analysis in 2,405 patients with a dystrophinopathy using the UMD-DMD database: a model of nationwide knowledgebase
Bibliographic record
Abstract
UMD-DMD France is a knowledgebase developed through a multicenter academic effort to provide an up-to-date resource of curated information covering all identified mutations in patients with a dystrophinopathy. The current release includes 2,411 entries consisting in 2,084 independent mutational events identified in 2,046 male patients and 38 expressing females, which corresponds to an estimated number of 39 people per million with a genetic diagnosis of dystrophinopathy in France. Mutations consist in 1,404 large deletions, 215 large duplications, and 465 small rearrangements, of which 39.8% are nonsense mutations. The reading frame rule holds true for 96% of the DMD patients and 93% of the BMD patients. Quality control relies on the curation by four experts for the DMD gene and related diseases. Data on dystrophin and RNA analysis, phenotypic groups, and transmission are also available. About 24% of the mutations are de novo events. This national centralized resource will contribute to a greater understanding of prevalence of dystrophinopathies in France, and in particular, of the true frequency of BMD, which was found to be almost half (43%) that of DMD. UMD-DMD is a searchable anonymous database that includes numerous newly developed tools, which can benefit to all the scientific community interested in dystrophinopathies. Dedicated functions for genotype-based therapies allowed the prediction of a new multiexon skipping (del 45-53) potentially applicable to 53% of the deleted DMD patients. Finally, such a national database will prove to be useful to implement the international global DMD patients' registries under development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".