Bibliographic record
Abstract
Progress in the treatment of cancers in young people has resulted in an increasing success rate in curing the different forms of malignant diseases. The mission of the CPAC/C(17) Task Force on Adolescents and Young Adults (AYA) with cancer is to ensure prompt, equitable access to the best care; establish research priorities to optimize health outcomes and health-related quality of life; and mitigate current disparities of care through advances in treatment, education, and research. Although these goals are important, the mission statement seems to ignore an important factor: "affordability," or the ability to achieve these goals due to scarcity of resources. In this article, the role of economics in helping decision makers decide on resource allocation is discussed. Also described is the economic basis for the healthcare problem; the inability of the current methodology of cost-effectiveness to provide information that can help improve resource allocation in health; and how economics should be used to promote efficient use of healthcare resources. The author argued that "affordability" should be recognized in the mission statement. Recognizing "affordability" means recognizing the need to justify the transfer (or allocation) of additional resources to AYA cancer, which requires demonstration that the value of what is gained from the use of these resources in AYA cancer exceeds the value of what is forgone by using them elsewhere. This will also require making explicit the values or equity criteria to which society subscribes.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".