Analysis of 2023 World Health Organization cancer Essential Medicines List and concordance with resource-stratified guidelines
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) Essential Medicines List (EML) and resource-stratified guidelines both prioritize high-value medicines. We compared the 2023 EML cancer medicines list with global cancer statistics, examined the proportion of EML therapies recommended by resource-stratified guidelines, and identified gaps that merit evaluation by the WHO EML Committee. METHODS: We compared the 2023 EML medicines for adult cancers with cancer incidence and mortality data from GLOBOCAN 2022. We cross-referenced the EML with 2 resource-stratified guidelines (National Comprehensive Cancer Network [NCCN] and National Cancer Grid [NCG] of India) and evaluated preferred treatments in resource-stratified guidelines that were not recommended by the EML. RESULTS: The 2023 EML included 64 cancer medicines for 219 tumor-specific indications. The greatest number of medicines are listed for leukemia (37/219 [17%]) and lymphoma (33/219 [15%]). Although some common cancers (eg, hepatocellular carcinoma) have no EML-listed medicines because of the low clinical benefit, we identified some cancers (eg, esophageal, gastric) with effective therapies that the EML Committee should evaluate for inclusion. Cancers with no listed medicines make up 34% of cancer deaths globally. Among EML indications with an NCCN resource-stratified guideline, 42% (35/84) and 73% (62/84) were recommended by the NCCN Basic and Core Guidelines, respectively. Among EML indications with an NCG resource-stratified guideline, 163 of 196 (83%) and 175 of 196 (89%) were recommended by the NCG Essential and Optimal guidelines, respectively. CONCLUSIONS: We identified some effective medicines that should be evaluated for inclusion in the WHO EML. Prioritization of cancer medicines was similar between the EML and NCG India but discordant between with NCCN resource-stratified guideline.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".