Characteristics and Outcomes of Clinical Trials on Gene Therapy in Noncongenital Cardiovascular Diseases: Cross-sectional Study of Three Clinical Trial Registries
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide. Gene therapies (GTs) may become a novel therapeutic option for cardiovascular diseases. OBJECTIVE: We aimed to characterize all trials involving human subjects utilizing GT to treat noncongenital cardiovascular diseases. METHODS: In March 2021, we searched for clinical trials on the ClinicalTrials.gov (CT), International Clinical Trials Registry Platform (ICTRP), and International Standard Randomised Controlled Trials Number (ISRCTN) databases. Two authors screened the titles and registry notes of all the searched studies. We collected details of the included studies regarding their design, location funding source, treated conditions, completion, publication statuses, and final outcomes. RESULTS: We generated a total of 3508 records, and 50 unique clinical trials met our eligibility criteria. Of these, 20 (40%) concerned peripheral artery disease, and 18 (36%) concerned coronary artery disease. Most studies were randomized (34/50, 68%) and were performed in multiple locations (30/50, 60%), and around half of the trials compared GT with a placebo (27/50, 54%), while one in four were single-arm (14/50, 28%), and the rest concerned dose-finding (22%). More than half of the trials (29/50, 58%) were funded by industry. Of the 50 clinical trials, 28 (56%) published their results by the data collection date (March 2021), and 22 of 31 (71%) were slated to be completed before 2021. Overall, 12 of 28 (42.9%) clinical trials showed favorable outcomes of the intervention. CONCLUSIONS: Among noncongenital cardiovascular diseases, GTs are mostly investigated in peripheral artery disease and coronary artery disease. Many clinical trials on GT use in noncongenital cardiovascular diseases did not disclose their results. Regardless of the trial phase, less than half of published studies on GT in noncongenital cardiovascular diseases showed promising results.
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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 | 0.072 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".