Access to Cytotoxic Medicines by Children With Cancer: A Focus on Low and Middle Income Countries
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it