Clinical trials in Asia: A World Health Organization database study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The Asian continent appears to be a growing destination for conducting cost-effective clinical trials, utilizing the available pool of treatment naïve subjects. This study aims to determine the growth rate of clinical trials in Asia. METHODS: A database review was conducted. Registered clinical trials conducted in selected countries in Asia, Europe, Australia, and North America between 2008 and 2017 were searched from the International Clinical Trial Registry Platform. Compound Annual Growth Rate was determined for registered clinical trials. RESULTS: During the 10-year period, a total of 125,918 registered clinical trials were conducted in Asia. There was a 7-fold increase in the number of registered clinical trials in Asia. More trials were registered in Japan than any other Asian country (30.8%). The average annual increase in the number of registered trials was generally higher in Asia than the United States of America, Canada, EU countries, or Australia. Iran recorded the highest average annual increase in the number of all clinical trials in Asia (41.9%). The number of pediatric clinical trials in Iran also increased annually by an average of 30.4%, more than any other country included in this study. CONCLUSIONS: Clinical trials recruitment in Asia is increasing faster than Europe, North America, and Australia. Lower trial cost and large patient pool may be contributory to this increase.
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 | 0.068 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.040 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".