Genotype-phenotype analysis in 2,405 patients with a dystrophinopathy using the UMD-DMD database: a model of nationwide knowledgebase
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 0.000 |
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 it