Eight years after an international workshop on myotonic dystrophy patient registries: case study of a global collaboration for a rare disease
Bibliographic record
Abstract
BACKGROUND: Myotonic Dystrophy is the most common form of muscular dystrophy in adults, affecting an estimated 10 per 100,000 people. It is a multisystemic disorder affecting multiple generations with increasing severity. There are currently no licenced therapies to reverse, slow down or cure its symptoms. In 2009 TREAT-NMD (a global alliance with the mission of improving trial readiness for neuromuscular diseases) and the Marigold Foundation held a workshop of key opinion leaders to agree a minimal dataset for patient registries in myotonic dystrophy. Eight years after this workshop, we surveyed 22 registries collecting information on myotonic dystrophy patients to assess the proliferation and utility the dataset agreed in 2009. These registries represent over 10,000 myotonic dystrophy patients worldwide (Europe, North America, Asia and Oceania). RESULTS: The registries use a variety of data collection methods (e.g. online patient surveys or clinician led) and have a variety of budgets (from being run by volunteers to annual budgets over €200,000). All registries collect at least some of the originally agreed data items, and a number of additional items have been suggested in particular items on cognitive impact. CONCLUSIONS: The community should consider how to maximise this collective resource in future therapeutic programmes.
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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 | 0.038 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| 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".