Adolescents and young adults with cancer: An orphaned population
Bibliographic record
Abstract
Adolescents and young adults (AYAs [15 to 29 years of age]) with cancer have a distinct cancer epidemiology, evolving hormonal milieu, maturing development, transitions in autonomy, increasing demands in education, entry into the workplace and family responsibilities. The prevalence of epithelial cancers in AYA patients represents a major shift from the embryonal cancers that predominate in early childhood. Thus, one would expect a specialized expertise to be required in caring for these patients, who typically fall between paediatric and oncology spheres of practice. Complex issues contribute to the lower survival rates noted for AYAs compared with those of younger patients, even with the same cancer. Cooperative group clinical trial participation has been crucial in advancing the excellent outcomes accomplished in paediatric oncology, yet participation by adolescents in clinical trials (either adult or paediatric) is typically low. There is increasing evidence that both appropriate location of care and access to specialists in paediatric or adult oncology contribute to favourable outcomes. Issues specific to AYA patients should be studied rigorously so that evidence-based approaches may be used to reduce waiting times, ensure prompt referral to appropriate centres, increase accrual to clinical trials, foster compliance, provide comprehensive supportive care and promote programs designed to enhance survivorship.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".