From Podium into Practice: Working Together to Revolutionise Cancer Care in the Real World
Bibliographic record
Abstract
Cancer is on course to be the leading cause of death in the EU by 2035. Europe’s population is ageing rapidly, and obesity rates continue to climb; meanwhile, healthcare systems struggle with delayed diagnoses and unequal access to treatments. Despite these challenges, the oncology community has many reasons to be optimistic. Our increased understanding of cancer has enabled us to create potentially transformative treatments, which could deliver life-changing outcomes that were unimaginable 20 years ago. Much of this emerging science has been showcased at this year’s European Society for Medical Oncology (ESMO) Congress, leading to well-warranted excitement across the community. However, it will take more than early research and positive clinical trial data to transform cancer care. We must embed new technologies and approaches in real health systems, looking 10 or 20 years ahead, to truly redefine cancer care. Greg Rossi, Senior VP, Head of Oncology, Europe and Canada, AstraZeneca, shares his thoughts on the critical approaches and concrete steps needed for progress and the importance of collaboration to achieve these goals.
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 | 0.000 | 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".