Unification of Efforts to Improve Global Access to Cancer Therapeutics: Report From the 2022/2023 Access to Essential Cancer Medicines Stakeholder Summit
Bibliographic record
Abstract
PURPOSE: There is an urgent need to improve access to cancer therapy globally. Several independent initiatives have been undertaken to improve access to cancer medicines, and additional new initiatives are in development. Improved sharing of experiences and increased collaboration are needed to achieve substantial improvements in global access to essential oncology medicines. METHODS: The inaugural Access to Essential Cancer Medicines Stakeholder Meeting was organized by ASCO and convened at the June 2022 ASCO Annual Meeting in Chicago, IL, with two subsequent meetings, Union for International Cancer Control World Cancer Congress held in Geneva, Switzerland, in October 2022 and at the ASCO Annual Meeting in June of 2023. Invited stakeholders included representatives from cancer institutes, physicians, researchers, professional societies, the pharmaceutical industry, patient advocacy organizations, funders, cancer organizations and foundations, policy makers, and regulatory bodies. The session was moderated by ASCO. Past efforts and current and upcoming initiatives were initially discussed (2022), updates on progress were provided (2023), and broad agreement on resulting action steps was achieved with participants. RESULTS: Summit participants recognized that while much work was ongoing to enhance access to cancer therapeutics globally, communication and synergy across projects and organizations could be enhanced by providing a platform for collaboration and shared expertise. CONCLUSION: The summit resulted in new cross-stakeholder insights and planned collaboration addressing barriers to accessing cancer medications. Specific actions and timelines for implementation and reporting were established.
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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 | 0.037 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".