Research challenges in adolescent and young adult cancer survivor research
Bibliographic record
Abstract
Every year in Canada and the United States, about 26,000 adolescent and young adults (AYA) between ages 15 and 29 years are diagnosed with cancer. Although the majority of AYA cancer patients will survive their primary cancer, many will develop serious health problems or die prematurely secondary to their curative cancer therapy. Much is known about the long-term health outcomes after adolescent cancer. In contrast, there remain substantial gaps in our understanding of the long-term outcomes after most young adult cancers. To optimize the health and quality of life of AYA cancer survivors and improve upon curative cancer therapy, it is essential to further investigate the long-term outcomes of this population. Before embarking upon this endeavor, it is important for the investigator and the funding agency to be cognizant about some of the unique challenges in research of AYA cancer survivors. To this end, the authors present a brief overview of some of the key research challenges, discuss the strengths and limitations of using available AYA cohorts and databases, and highlight potential future directions.
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 | 0.270 | 0.330 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".