Computerized monitoring of COVID-19 trials, studies and registries in ClinicalTrials.gov registry
Bibliographic record
Abstract
Clinical trial registries can provide important information about relevant studies for a given condition to other researchers and the public. We developed a computerized informatics based approach to provide an overview and analysis of COVID-19 studies registered on ClinicalTrials.gov registry. Using the perspective of analyzing active or completed COVID-19 studies, we identified 401 interventional clinical trials, 287 observational studies and 64 registries. We analyzed features of each study type separately such as location, design, interventions and update history. Our results show that the United States had the most COVID-19 interventional trials, France had the most COVID-19 observational studies and France and the United States tied for the most COVID-19 registries on ClinicalTrials.gov. The majority of studies in all three study types had a single study site. For update history "Study Status" is the most updated information and we found that studies located in Canada (2.70 updates per study) and the United States (1.76 updates per study) update their studies more often than studies in any other country. Using normalization and mapping techniques, we identified Hydroxychloroquine (92 studies) as the most common drug intervention, while convalescent plasma (20 studies) is the most common biological intervention. The primary purpose of most interventional trials is for treatment with 298 studies (74.3%). For COVID-19 registries we found the most common proposed follow-up time is 1 year (15 studies). Of specific importance and interest is COVID-19 vaccine trials, of which 12 were identified. Our informatics based approach allows for constant monitoring and updating as well as multiple applications to other conditions and interests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".