Access to innovative therapies in pediatric oncology: Report of the nationwide experience in Canada
Bibliographic record
Abstract
BACKGROUND: The need for new therapies to improve survival and outcomes in pediatric oncology along with the lack of approval and accessible clinical trials has led to "out-of-trial" use of innovative therapies. We conducted a retrospective analysis of requests for innovative anticancer therapy in Canadian pediatric oncology tertiary centers for patients less than 30 years old between 2013 and 2020. METHODS: Innovative therapies were defined as cancer-directed drugs used (a) off-label, (b) unlicensed drugs being used outside the context of a clinical trial, or (c) approved drugs with limited evidence in pediatrics. We excluded cytotoxic chemotherapy, cellular products, and cytokines. RESULTS: We retrieved data on 352 innovative therapy drug requests. Underlying diagnosis was primary CNS tumor 31%; extracranial solid tumor 37%, leukemia/lymphoma 22%, LCH 2%, and plexiform neurofibroma 6%. RAS/MAP kinase pathway inhibitors were the most frequently requested innovative therapies in 28% of all requests followed by multi-targeted tyrosine kinase inhibitors (17%), inhibitors of the PIK3CA-mTOR-AKT pathway (8%), immune checkpoints inhibitors (8%), and antibody drug conjugates (8%). In 112 out of 352 requests, innovative therapies were used in combination with another anticancer agent. 48% of requests were motivated by the presence of an actionable molecular target. Compassionate access accounted for 52% of all requests while public insurance was used in 27%. Mechanisms of funding varied between provinces. CONCLUSION: This real-world data collection illustrates an increasing use of "out-of-trial" innovative therapies in pediatric oncology. This new field of practice warrants further studies to understand the impact on patient trajectory and equity in access to innovative therapies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".