Access to Cytotoxic Medicines by Children With Cancer: A Focus on Low and Middle Income Countries
Bibliographic record
Abstract
BACKGROUND: The Essential Medicines Working Group of the International Society of Pediatric Oncology (SIOP) has proposed a list of antineoplastic drugs that should be available in low and middle income countries. PROCEDURE: Data were extracted on the listing of 18 essential and 8 ancillary antineoplastic medicines in the national essential medicines lists (NEMLs) or national reimbursable medicines lists (NRMLs) of 135 countries with gross national income (GNI) per capita of less than US $25,000. Correlations between numbers of medicines listed and GNI per capita, annual government health expenditure (AGHE) per capita, and the number of physicians per million people were examined. RESULTS: Listing of the 18 essential antineoplastic drugs ranged from 27% (thioguanine) to 95% (methotrexate). The median number of medicines listed was 7 (0-18) in low income countries (n = 26) and 14 in lower-middle (n = 42), upper-middle (n = 44), and high income countries (n = 20). For the ancillary eight medicines, the median was one (0-8) across the 135 countries. Correlations with GNI per capita (r = 0.17, P = 0.0266) and physician density (r = 0.25, P = 0.0017) were statistically significant; not so for AGHE per capita (r = 0.00, P = 0.5000). CONCLUSIONS: There was large variability within income groups in numbers of antineoplastic agents identified as essential in NEMLs and NRMLs. While not a direct measure of availability, listing is an important step, guiding procurement for the public sector. These results focus attention on deficits in NEMLs and NMRLs as a step to improving access to effective antineoplastic medicines for cancers in children in low and middle income countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".