Essential cancer medicines and cancer outcomes: Cross‐sectional study of 124 countries
Bibliographic record
Abstract
BACKGROUND: Cancer is the second leading cause of death worldwide. Alongside other interventions, access to certain medicines may decrease cancer-associated mortality. Listing medicines on national essential medicines lists may improve health outcomes. We examine the association between cancer mortality amenable to care and the listing of cancer medicines on national essential medicines lists (NEMLs) of 124 countries. METHODS: In this cross-sectional study, we determined the number of medicines used to treat eight cancers on NEMLs and used multiple linear regression to analyze the association between cancer health outcome scores and the number of medicines on NEMLs while controlling for GDP. A sensitivity analysis was also conducted using selected medicines. FINDINGS: The number of cancer medicines on NEMLs was not associated with cancer health outcome scores when GDP was controlled for non-melanoma skin (p = 0.224), uterine (p = 0.221), breast (p = 0.145), Hodgkin's lymphoma (p = 0.697), colon (p = 0.299), leukemia (p = 0.103), cervical (p = 0.834), and testicular cancers (p = 0.178). INTERPRETATION: There was a weak association between listing medicines for eight cancers in NEMLs and amenable mortality. Further studies are required to explore association between cancer health outcomes and other factors such as actual availability of medicines listed, access to surgeries, accurate diagnosis, radiotherapy, and early detection.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".