The Anti-Coronavirus Therapies (ACT) Trials: Design, Baseline Characteristics, and Challenges
Bibliographic record
Abstract
Background: Effective treatments for COVID-19 are urgently needed, but conducting randomized trials during the pandemic has been challenging. Methods: The Anti-Coronavirus Therapy (ACT) trials are parallel factorial international trials that aimed to enroll 3500 outpatients and 2500 inpatients with symptomatic COVID-19. The outpatient trial is evaluating colchicine vs usual care, and aspirin vs usual care. The primary outcome for the colchicine randomization is hospitalization or death, and for the aspirin randomization, it is major thrombosis, hospitalization, or death. The inpatient trial is evaluating colchicine vs usual care, and the combination of rivaroxaban 2.5 mg twice daily and aspirin 100 mg once daily vs usual care. The primary outcome for the colchicine randomization is need for high-flow oxygen, need for mechanical ventilation, or death, and for the rivaroxaban plus aspirin randomization, it is major thrombotic events, need for high-flow oxygen, need for mechanical ventilation, or death. Results: At the completion of enrollment on February 10, 2022, the outpatient trial had enrolled 3917 patients, and the inpatient trial had enrolled 2611 patients. Challenges encountered included lack of preliminary data about the interventions under evaluation, uncertainties related to the expected event rates, delays in regulatory and ethics approvals, and in obtaining study interventions, as well as the changing pattern of the COVID-19 pandemic. Conclusions: The ACT trials will determine the efficacy of anti-inflammatory therapy with colchicine, and antithrombotic therapy with aspirin given alone or in combination with rivaroxaban, across the spectrum of mild, moderate, and severe COVID-19. Lessons learned from the conduct of these trials will inform planning of future trials.
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 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 | 0.314 | 0.230 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".