Current State of Hypertrophic Cardiomyopathy Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Hypertrophic cardiomyopathy (HCM) is a genetic disorder with a very large global burden for which more therapeutic management regimens are required. OBJECTIVES: In this study, the authors explore HCM-related clinical trials, determine the shortcomings leading to the lack of development of new HCM therapies, and attempt to shed light on potential areas for improvement. METHODS: In January 2019, the authors completed a search on ClinicalTrials.gov for all therapeutic and interventional clinical trials involving HCM, without any limits for location or date. Information on trial characteristics such as phase, start and end dates, sample size, experimental intervention, publications, study design, selection criteria, and results were collected and analyzed. RESULTS: Sixty-three trials met the selection criteria. The average trial duration across phases was around 3 years. Around one-half of the trials were conducted in North America (United States and Canada) and 44% of the trials were in their early phases (I and II). Approximately one-third of the trials were completed. Only 14 publications were produced from all the clinical trials studied. CONCLUSIONS: The study revealed a low number of trials, lack of geographic diversity, and scarcity of published results concerning HCM clinical trials. Proper management of HCM trials is of vast importance to achieve effective therapies.
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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 | 0.036 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".