Proposing Essential Medicines to Treat Cancer: Methodologies, Processes, and Outcomes
Bibliographic record
Abstract
PURPOSE: A great proportion of the world's cancer burden resides in low- and middle-income countries where cancer care infrastructure is often weak or absent. Although treatment of cancer is multidisciplinary, involving surgery, radiation, systemic therapies, pathology, radiology, and other specialties, selection of medicines that have impact and are affordable has been particularly challenging in resource-constrained settings. In 2014, at the invitation of the WHO, the Union for International Cancer Control convened experts to develop an approach to propose essential cancer medicines to be included in the WHO Model Essential Medicines Lists (EML) for Adults and for Children, as well as a resulting new list of cancer medicines. METHODS: Experts identified 29 cancer types with potential for maximal treatment impact, on the basis of incidence and benefit of systemic therapies. More than 90 oncology experts from all continents drafted and reviewed disease-based documents outlining epidemiology, diagnostic needs, treatment options, and benefits and toxicities. RESULTS: Briefing documents were created for each disease, along with associated standard treatment regimens, resulting in a list of 52 cancer medicines. A comprehensive application was submitted as a revision to the existing cancer medicines on the WHO Model Lists. In May 2015, the WHO announced the addition of 16 medicines to the Adult EML and nine medicines to the Children's EML. CONCLUSION: The list of medications proposed, and the ability to link each recommended medicine to specific diseases, should allow public officials to apply resources most effectively in developing and supporting nascent or growing cancer treatment programs.
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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 | 0.159 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".