Clinical trials in India: Where do we stand globally?
Bibliographic record
Abstract
AIMS: To evaluate the trend of clinical trials in India over the last 4 years compared to the well-established countries using clinical trial registries since the advent of clinical trial registry of India (CTRI). MATERIALS AND METHODS: The data of clinical trials registered in India, United States (US), and European Union (EU) were obtained from websites of CTRI, clinicaltrial.gov and EU clinical trial registry, respectively from July 20, 2007 to August 29, 2011 for a period of 4 years. Trials registered in Australia, Canada, China, and Japan were obtained from WHO's international clinical trial registry platform for the same period. We used search words for the common diseases such as diabetes, hypertension, etc.. RESULTS: The total number of clinical trials registered during the study period was 67,448 across seven study nations. Clinical trials from India constituted only 2.7% of the total number of trials carried out, compared to US constituting 47% of the total number of trials registered, followed by 18% from EU and 11% from Japan. However, India, China, and Japan have been found to show an increase of 3.7%, 5.1%, and 13.1% increase in the number of trials registered in 2011 compared to 2007. In contrast, US and EU showed a decline of 11.3% and 11.95% respectively in the total number of trials registered in 2011 compared to 2007. CONCLUSIONS: Although India shows gradual increase in trials registered since the advent of CTRI, still it continues to lag behind established countries in clinical research.
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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 | 0.090 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.024 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".