Mental Disorders Among Adolescents and Young Adults With Cancer: A Canadian Population–Based and Sibling Cohort Study
Bibliographic record
Abstract
PURPOSE: To compare the cumulative incidence of mental disorders among adolescents and young adults (AYAs) diagnosed with cancer with the general population and their unaffected siblings. METHODS: A retrospective, population-based, matched cohort design was used to investigate the impact of cancer diagnosis on mental disorders among individuals age 15-39 diagnosed between 1989 and 2019. Two cancer-free cohorts were identified: matched population-based and sibling cohorts. Outcomes included incidence of mood and anxiety disorders, substance use disorders, suicide outcomes, psychotic disorders, and any of the preceding four categories within 5 years of cancer diagnosis. Competing risk regression was used to estimate adjusted subhazard ratios (aSHR) and 95% CIs. RESULTS: Among 3,818 AYAs with cancer matched to the population-based cancer-free cohort, individuals with cancer were more likely to be diagnosed with incident mental disorders than those without cancer; the risk was highest immediately after a cancer diagnosis and decreased over time with aSHR [95% CI] for mood and anxiety disorders at 0-6 months (11.27 [95% CI, 6.69 to 18.97]), 6-12 months (2.35 [95% CI, 1.54 to 3.58]), and 12-24 months (2.06 [95% CI, 1.55 to 2.75]); for substance use disorders at 0-6 months (2.73 [95% CI, 1.90 to 3.92]); for psychotic disorders at 0-6 months (4.69 [95% CI, 2.07 to 10.65]); and for any mental disorder at 0-6 months (4.46 [95% CI, 3.41 to 5.85]), 6-12 months (1.56 [95% CI, 1.14 to 2.14]), and 12-24 months (1.7 [95% CI, 1.36 to 2.13]) postcancer diagnosis. In sibling comparison, cancer diagnosis was associated with a higher incidence of mood and anxiety and any mental disorder during first 6 months of cancer diagnosis. CONCLUSION: AYAs with cancer experience a greater incidence of mental disorders after cancer diagnosis relative to population-based and sibling cohorts without cancer, primarily within first 2 years, underscoring the need to address mental health concerns during this period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".