Defining Essential Childhood Cancer Medicines to Inform Prioritization and Access: Results From an International, Cross-Sectional Survey
Bibliographic record
Abstract
PURPOSE: Access to essential cancer medicines is a major determinant of childhood cancer outcomes globally. The degree to which pediatric oncologists deem medicines listed on WHO's Model List of Essential Medicines for Children (EMLc) essential is unknown, as is the extent to which such medicines are accessible on the front lines of clinical care. METHODS: An electronic survey developed was distributed through the International Society of Pediatric Oncology mailing list to members from 87 countries. Respondents were asked to select 10 cancer medicines that would provide the greatest benefit to patients in their context; subsequent questions explored medicine availability and cost. Descriptive and bivariate statistics compared access to medicines between low- and lower-middle-income countries (LMICs), upper-middle-income countries (UMICs), and high-income countries (HICs). RESULTS: Among 159 respondents from 44 countries, 43 (27%) were from LMICs, 79 (50%) from UMICs, and 37 (23%) from HICs. The top five medicines were methotrexate (75%), vincristine (74%), doxorubicin (74%), cyclophosphamide (69%), and cytarabine (65%). Of the priority medicines identified, 87% (27 of 31) are represented on the 2021 EMLc and 77% (24 of 31) were common to the lists generated by LMIC, UMIC, and HIC respondents. The proportion of respondents indicating universal availability for each of the top medicines ranged from 9% to 46% for LMIC, 25% to 89% for UMIC, and 67% to 100% for HIC. Risk of catastrophic expenditure was more common in LMIC (8%-20%), compared with UMIC (0%-28%) and HIC (0%). CONCLUSION: Most medicines that oncologists deem essential for childhood cancer treatment are currently included on the EMLc. Barriers remain in access to these medicines, characterized by gaps in availability and risks of catastrophic expenditure for families that are most pronounced in low-income settings but evident across all income contexts.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".