Global Cancer Drug Development—A Report From the 2022 Accelerating Anticancer Agent Development and Validation Meeting
Bibliographic record
Abstract
Rapidly expanding systemic treatment options, combined with improved screening, diagnostic, surgical, and radiotherapy techniques, have led to improved survival outcomes for many cancers over time. However, these overall survival gains have disproportionately benefited patients in high-income countries, whereas patients in low- and middle-income countries (LMICs) continue to experience challenges in accessing timely and guideline concordant care. In September 2022, the Accelerating Anticancer Agent Development and Validation workshop was held, focusing on global cancer drug development. Panelists discussed key barriers such as the lack of diagnostic services and human resources, drug accessibility and affordability, lack of research infrastructure, and regulatory and authorization challenges, with a particular focus on Africa and Latin America. Potential opportunities to improve access and affordability were reviewed, such as the importance of prioritizing investments in diagnostics, investing health infrastructure and work force planning, coordinated drug procurement efforts and streamlined regulatory processing, incentivized pricing through regulatory change, and the importance of developing and promoting clinical trials that can answer relevant clinical questions for patients in LMICs. As a cancer community, we must continue to advocate for and work toward equitable access to high-quality interventions for patients, regardless of their geographical location.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".