Establishing Biomarkers and Clinical Endpoints in Myotonic Dystrophy Type 1 (END-DM1): protocol of an international natural history study
Bibliographic record
Abstract
Abstract Background Myotonic dystrophy type 1 (DM1) is an autosomal dominant inherited multi-system disorder that affects skeletal muscles but also many other organ systems. Molecular targets have been identified and targeted therapies are being developed and tested in first-in-human clinical trials. However, insufficient knowledge of the phenotypic heterogeneity and natural course of the disease, together with a lack of reliable biomarkers, complicate the design of clinical trials. Methods The main objectives of this study are to 1) characterize the phenotypic heterogeneity and disease progression of DM1 in a large cohort; 2) identify baseline characteristics that predict subsequent progression; 3) validate RNA biomarkers of disease severity. This is a prospective, multi-site observational study with a follow-up period of 24 months including approximately 700 adult DM1 patients. Visits will occur at baseline, and months 12 and 24. All patients will undergo strength testing, myotonia assessment, a battery of functional outcome assessments, spirometry, and complete various questionnaires and cognitive tests. Blood and urine samples will be taken at each visit for biomarker studies. A subset of 60 patients will undergo a muscle biopsy at baseline and at an additional 3-month visit. The sensitivity to disease progression and minimally clinically important differences will be determined for the various clinical outcome measures. Associations between baseline patient characteristics and the rate of disease progression will be evaluated. Discussion The results of this large international study on DM1 will contribute to optimizing clinical trial design. Both data and biological samples will be collected for future research as well. Trial registration clinicaltrials.gov NCT03981575
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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 | 0.073 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".