A toolkit for new facioscapulohumeral muscular dystrophy trial sites
Bibliographic record
Abstract
Numerous potential treatments are being developed for facioscapulohumeral muscular dystrophy (FSHD). Project Mercury was initiated to overcome challenges that could slow or prevent effective therapies from widespread availability to patients. It is important that upcoming trials include trial sites from different countries. We share our lessons learnt in clinical trials to assist inexperienced sites to become eligible for upcoming clinical trials. To become an eligible site, several key elements need to be in place such as personnel, facilities, and accessible patient populations. Clinical trial networks, patient advocacy groups and patient registries can support new sites in establishing these elements. As the preparation, execution and close-out of clinical trials generally involve the same steps every time, it is recommended to create and follow a trial roadmap. Most clinical trials are sponsor-initiated and involve working closely with the sponsor and vendors. It is therefore important to understand each other perspectives and goals for each trial. Once a drug receives regulatory approval and becomes available for market use new challenges arise such as patient reimbursement and phase 4 surveillance of the patients. In summary, we are at a pivotal time for FSHD and other rare neuromuscular disorders with the development of new disease modifying therapies. It is vital that as many sites as possible can participate in upcoming trials.
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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 | 0.114 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.141 | 0.074 |
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".