Childhood cancer drugs in China: An overview and comparison of regulatory approvals in China and the United States
Bibliographic record
Abstract
Different from less developed countries, 80% of children with cancers in the United States are cured. Traditional chemotherapy drugs are the mainstay of therapies; new targeted medications have become available recently. Using publicly available data, we created a database of cancer drugs with paediatric malignancy indications approved by 31 October 2020 in China and the United States. We compared numbers, type, indications and listing on the World Health Organization Model List of Essential Medicines for Children (WHO EMLc) between the two countries, assessed the correlation between paediatric indications and cancer incidences, and described evidence supporting approvals of targeted medications in the two settings. Our study showed that by 31 October 2020, 31 and 39 cancer drugs available in China and the United States were approved for use in children, corresponding to 137 and 102 paediatric cancer indications, respectively. About half of these drugs (17 in China and 18 in the United States) were listed on the WHO EMLc. The correlation between indications and burden of disease was higher in the United States (r = 0.68) than China (r = 0.59). More traditional chemotherapy drugs were approved in China (n = 27) than the United States (n = 19). Of 20 targeted childhood anticancer medicines approved in the United States, mainly on the basis of single arm trials (27/32 indications, 84.4%), only four were approved for paediatric indications in China, at a median of 2.8 years after US Food and Drug Administration approval. A harmonised, evidence-based regulatory framework is needed to ensure approvals of needed, safe and efficacious childhood cancer drugs across the world.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".