Clinical Cancer Advances 2021: ASCO's Report on Progress Against Cancer
Bibliographic record
Abstract
Clinical Cancer Advances 2021: ASCO's Report on Progress Against Cancer highlights the most important clinical research advances of the past year and identifies priority areas where ASCO believes research efforts should be focused moving forward.This year's report also discusses the critical issue of health equity in cancer research and solutions to ensure that every patient with cancer, everywhere, can access the latest advances. Achieving Equity in Cancer ResearchOverall cancer mortality has decreased in the United States 1 thanks to tremendous progress in cancer prevention, early detection, and treatment-underpinned by decades of research progress.2 Unfortunately, not all individuals with cancer have benefited equitably from this success, as Blacks, 3 patients living in rural areas, 4 populations with lower income and education levels, 5 and others continue to experience lower survival and higher mortality rates for many cancers.As clinicians, we are committed to providing evidenced-based, high-quality cancer care to every patient, every day, everywhere.But, if clinical trials don't represent the individuals we treat, including those from racial, ethnic, and other minority populations, the state of science suffers, and patients with life-threatening conditions may not receive the best-perhaps only-treatment option for their condition.
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 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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.076 | 0.048 |
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".