Long-Term Mental Health Service Utilization Among Survivors of Testicular Cancer: A Population-Based Cohort Study
Bibliographic record
Abstract
PURPOSE: Testicular cancer survivors may experience mental illness as a consequence of their cancer diagnosis and treatment. METHODS: All incident cases of testicular cancer treated with orchiectomy in Ontario, Canada (2000-2010), were identified using the Ontario Cancer Registry. Cases were matched to controls in a 1:5 ratio on age and geography. Population-level databases were used to identify mental health service use episodes; outpatient use included visits to a general practitioner for a mental health concern or any visit to a psychiatrist. Negative binomial regression modeling was used to estimate the rate of mental health service use in the pretreatment (2 years prior until 1 month before orchiectomy), peritreatment (1 month before until 1 month after orchiectomy), and post-treatment periods (1 month after orchiectomy until end of follow-up). Rate ratios (RR) comparing cases with controls in the peri- and post-treatment periods were adjusted for baseline mental health service use. RESULTS: Two thousand six hundred nineteen cases of testicular cancer were matched to 13,095 controls. There was no baseline difference in the rate of mental health service use. Cases were significantly more likely than controls to have an outpatient visit for a mental health concern in the peritreatment (adjusted RR [aRR], 2.45; 95% CI, 2.06 to 2.92) and post-treatment periods (aRR, 1.30; 95% CI, 1.12 to 1.52). The difference in mental health service use persisted over a median follow-up of 12 years. In the postorchiectomy period, cases with baseline mental health service use were those most likely to use mental health services (aRR, 5.64; 95% CI, 4.64 to 6.85). CONCLUSION: Testicular cancer survivors use mental health services more often than healthy controls. Survivorship care plans that address the long-term mental healthcare needs of this population are needed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".