Early phase oncology clinical trials in Malaysia: current status and future perspectives
Bibliographic record
Abstract
Historically, the majority of oncology clinical trials are conducted in Western Europe and North America. Globalization of drug development has resulted in sponsors shifting their focus to the Asia-Pacific region. In Malaysia, implementation of various government policies to promote clinical trials has been initiated over a decade ago and includes the establishment of Clinical Research Malaysia, which functions as a facilitator and enabler of industry-sponsored clinical trials on a nationwide basis. Although oncology clinical trials in Malaysia have seen promising growth, there are still only a limited number of early phase oncology studies being conducted. Hence, the Phase 1 Realization Project was initiated to develop Malaysia's early phase clinical trial capabilities. In addition, the adaptation of good practices from other countries contribute to the effective implementation of existing initiatives to drive progress in the development of early phase drug development set up in Malaysia. Furthermore, holistic approaches with emphasis in training and education, infrastructure capacities, strategic alliances, reinforcement of upstream activities in the value chain of drug development, enhanced patient advocacy, coupled with continued commitment from policy makers are imperative in nurturing a resilient clinical research ecosystem in Malaysia.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".