Looking Back to Move Forward: Lessons Learned from a Successful, Sustainable, Replicable Model of Adolescent and Young Adult Program of a Tertiary Cancer Care Center
Bibliographic record
Abstract
Background: The Princess Margaret Cancer Centre (PM) established the adolescent and young adult (AYA) oncology program in 2014 to address the unique needs of AYA by delivering targeted, evidence-based care through a multidisciplinary team. Methods: We performed a retrospective analysis of patients who underwent a consultation with the PM AYA program from 2014 to 2020. The association between the health domain concerns reported and age at consultation, cancer diagnoses, and time since diagnosis was analyzed using chi-square test of independence in SPSS. Results: In our cohort of 1128 AYA, the median age at assessment was 28.2 years. The most common diagnoses were lymphoma ( n = 251, 22.2%), leukemia ( n = 207, 18.4%), and breast cancer ( n = 162, 14.4%). The most common concerns reported were related to fertility ( n = 882, 78.2%) and work/school ( n = 472, 41.8%). Fertility concerns were most common in 25–34 age group (443/540, 82.0%) and work-/school-related concerns were highest in 18–24 age group (191/355, 53.8%). Diagnoses significantly affect majority of concerns reported. Fertility concerns were most common in AYA consulted near diagnosis, while body image-, exercise-, and diet-related concerns were more frequently reported, while on active treatments. Conclusions: Supporting fertility concerns remains the cornerstone of any successful AYA program. Work-/school-related concerns deserve more elucidation and attention. We identified important patterns in the health-related concerns of AYA, especially as they relate to age, diagnoses, and time since diagnosis. This insight will guide us for improving patient-centered care delivery to AYA.
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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 | 0.050 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".