Cancer medicines on the WHO Model List of Essential Medicines: processes, challenges, and a way forward
Bibliographic record
Abstract
The selection of cancer medicines for national procurement requires deliberate evaluation of population benefit, budget impact, sustainability, and health system capacity. However, this process is complicated by numerous challenges, including the large volume and rapid pace of newly developed therapies offering marginal gains at prohibitively high prices. The WHO Model List of Essential Medicines (EML) and Model List of Essential Medicines for Children (EMLc) have undergone a series of evidence-based updates to ensure recommended cancer medicines offer meaningful clinical benefit. This Health Policy paper describes how cancer medicines are listed on the EML and EMLc, including two updated WHO processes: (1) the formation of the Cancer Medicines Working Group, and (2) additional selection principles for recommending cancer medicines, including a minimum overall survival benefit of 4-6 months with improvement to quality of life compared with standard treatment. These updates, along with proposals to include formal price considerations, additional selection criteria, and multisectoral collaboration (eg, voluntary licensing) promote procurement of high-value essential cancer medicines on national formularies in the context of supporting sustainable health systems to achieve universal health coverage.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".