Baseline results of a living systematic review for COVID-19 clinical trial registrations
Bibliographic record
Abstract
Background: Since the coronavirus disease 2019 (COVID-19) outbreak was first reported in December 2019, many independent trials have been planned that aim to answer similar questions. Tools allowing researchers to review studies already underway can facilitate collaboration, cooperation and harmonisation. The Infectious Diseases Data Observatory (IDDO) has undertaken a living systematic review (LSR) to provide an open, accessible and frequently updated resource summarising characteristics of COVID-19 study registrations. Methods: Review of all eligible trial records identified by systematic searches as of 3 April 2020 and initial synthesis of clinical study characteristics were conducted. In partnership with Exaptive , an open access, cloud-based knowledge graph has been created using the results. Results: There were 728 study registrations which met eligibility criteria and were still active. Median (25 th , 75 th percentile) sample size was 130 (60, 400) for all studies and 134 (70, 300) for RCTs. Eight lower middle and low income countries were represented among the planned recruitment sites. Overall 109 pharmacological interventions or advanced therapy medicinal products covering 23 drug categories were studied. Majority (57%, 62/109) of them were planned only in one study arm, either alone or in combination with other interventions. There were 49 distinct combinations studied with 90% (44/49) of them administered in only one or two study arms. The data and interactive platform are available at https://iddo.cognitive.city/ . Conclusions: Baseline review highlighted that the majority of investigations in the first three months of the outbreak were small studies with unique treatment arms, likely to be unpowered to provide solid evidence. The continued work of this LSR will allow a more dependable overview of interventions tested, predict the likely strength of evidence generated, allow fast and informative filtering of relevant trials for specific user groups and provide the rapid guidance needed by investigators and funders to avoid duplication of efforts.
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 | 0.165 | 0.525 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.025 | 0.036 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.110 | 0.014 |
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".