International collaboration to improve knowledge on myotonic dystrophy type 2
Bibliographic record
Abstract
BACKGROUND: The TREAT-NMD Global Registry Network is a global collaboration of neuromuscular disease registries, including myotonic dystrophy type 2 (DM2), which aims to facilitate collaborative research and clinical trials. OBJECTIVES: This study aimed to assess DM2 patients included in the network, and to analyse their socio-demographic and clinical features. METHODS: Data were collected through email surveys sent to 16 TREAT-NMD myotonic dystrophy core member registries. 10 registries enrolled DM2 patients. RESULTS: The total number of DM2 cases was 1,720, with the Czech, German, and USA registries enrolling the most patients (445, 430, and 339 cases, respectively). The highest rates were seen in Czechia and Serbia (4.2 and 2.0 registered per 100,000 population, respectively). High DM2:DM1 ratios were seen in Central Europe. The median age at registry entry was 51 years. Symptom onset occurred before age 20 in 14% of cases. One fifth of patients used an assistive device to walk, and 4% were non-ambulatory. Insertion of a pacemaker or implantable cardioverter-defibrillator was reported in 4% of subjects, while 7% used non-invasive ventilation. CONCLUSIONS: This represents the largest DM2 cohort assembled to date, providing demographic and clinical data for future research and trial recruitment, illustrating TREAT-NMD's international reach and the importance of capturing DM2 data.
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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 | 0.039 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".