How to build a better clinical trial ecosystem for future infectious disease emergencies in Japan: Findings from a narrative review and stakeholder meetings
Bibliographic record
Abstract
The COVID-19 pandemic posed a serious challenge to national and global pandemic preparedness and response (PPR). Timely identification and development of diagnostics, therapeutics and vaccines through prompt evidence generation from clinical trials was recognized as an important health security agenda. In 2022, under the guidance of Japan Ministry of Health, Labour and Welfare (MHLW), a health policy research team was convened to analyze the COVID-19 related clinical trial ecosystem in the context of PPR in Japan and abroad with a focus on clinical trials for therapeutics. The research mainly composed of the following: a narrative review of relevant peer reviewed journals and grey literature, interview of global experts and stakeholders including those from the United States and the United Kingdom, and a culminating meeting in Japan with various stakeholders. Based on the outcomes of this research, the team makes the following three recommendations: (1) Strengthen the leadership group's role in infectious disease clinical trials, (2) Promote sustained coordination and collaboration among stakeholders, and (3) Apply innovative clinical trial designs and create an enabling research environment. Clinical trials, as a public health good, must be further integrated into healthcare. The team advocates for the implementation of these recommendations at the policy level to help improve the clinical trial ecosystem for future health emergencies in Japan.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".