Psychiatric Disorders in Adolescent and Young Adult-Onset Cancer Survivors: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Although a cancer diagnosis during the adolescent and young adult (AYA) years is a traumatic event, and psychiatric disorders generally manifest during the AYA period, the impact of a cancer diagnosis on long-term mental health outcomes in this population is not well characterized. We conducted a systematic review and meta-analysis to determine if survivors of AYA cancers are more likely to develop psychiatric disorders. A systematic literature search of five databases, MEDLINE, CINAHL, Web of Science, EMBASE, and PsycINFO, was conducted from their inception to November 2018. The outcome measures were psychiatric disorders as per the Diagnostic Statistical Manual criteria, or psychiatric medication use. Study eligibility, appraisal, and data abstraction were independently conducted by two reviewers. Of 7934 total studies, four met eligibility criteria for the systematic review, three of which were included in the meta-analysis. Compared to cancer-free controls, survivors were at an elevated risk of mood disorders (odds ratio [OR] 1.36; 95% CI 1.19-1.55) and anxiety disorders (OR 1.16; 95% CI 1.05-1.28), but not substance-related disorders, (OR 0.88; 95% CI 0.63-1.22). The most commonly identified risk factors were the female sex and older age at diagnosis. We found higher odds of anxiety and mood disorders in AYA-onset cancer survivors. However, few AYA-specific studies currently exist that analyze psychiatric disorders using consistent and standardized methods. Additional studies confirming these findings are warranted.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".