Barriers to Oncofertility Care among Female Adolescent Cancer Patients in Canada
Bibliographic record
Abstract
High survival rates in adolescent cancer patients have shifted the medical focus to the long-term outcomes of cancer treatments. Surgery, chemotherapy, and radiation increase the risk of infertility and infertility-related distress in adolescent cancer patients and survivors. The aims of this narrative review were to (1) describe the psychosocial impacts of cancer-related infertility in adolescents, (2) identify multilevel barriers to fertility preservation (FP) conversations and referrals, and (3) conclude with evidence-based clinical solutions for improving the oncofertility support available to Canadian adolescents. The results of this review revealed that FP decisions occur within the patient, parent, and health care provider (HCP) triad, and are influenced by factors such as parent attitudes, patient maturity, and HCP knowledge. Decision tools and HCP education can promote the occurrence of developmentally appropriate fertility discussions. At the systems level, cost and resource barriers prevent patients from receiving sufficient fertility information and referrals. Clinical models of care (MOCs) can define interdisciplinary roles and referral pathways to improve the integration of oncofertility services into adolescent cancer care. The continued integration of oncofertility care will ensure that all Canadian adolescents receive the exemplary medical and psychological support necessary to make empowered decisions about their own fertility.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".