Active therapy and models of care for adolescents and young adults with cancer
Bibliographic record
Abstract
The reduction in the cancer mortality rate in adolescents and young adults (AYA) with cancer has lagged behind the reduction noted in children and older adults. Studies investigating reasons for this are limited but causes appear to be multifactorial. Host factors such as developmental stage, compliance, and tolerance to therapy; provider factors such as lack of awareness of cancer in AYA and referral patterns; differences in disease biology and treatment strategies; low accrual onto clinical trials; and lack of psychosocial support and education programs for AYA all likely play a role. Recommendations for change from a recent international workshop include education of physicians and patients concerning AYA cancer, improved cooperation between pediatric and adult centers, age-appropriate psychosocial support services, programs to help AYA with issues relevant to them, dedicated AYA hospital space, improved accrual to clinical trials, the use of technology to educate patients and enhance communication between patients and the health care team, and ensuring that resident and fellowship training programs provide adequate education in AYA oncology. The longer term goal is to develop AYA oncology into a distinct subspecialist discipline within oncology. The ideal model of care would incorporate medical care, psychosocial support services, and a physical environment that are age-appropriate. When this is not feasible, the development of "virtual units" connecting patients to the health care team or a combination of physical and virtual models are alternative options. The assessment of outcome measures is necessary to determine whether the interventions implemented result in improved survival and better quality of life, and are cost-effective.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".