Use of Medications for Treating Anxiety or Depression among Testicular Cancer Survivors: A Multi-Institutional Study
Bibliographic record
Abstract
Abstract Background: This study examined sociodemographic factors, cisplatin-related adverse health outcomes (AHO), and cumulative burden of morbidity (CBMPt) scores associated with medication use for anxiety and/or depression in testicular cancer survivors (TCS). Methods: A total of 1,802 TCS who completed cisplatin-based chemotherapy ≥12 months previously completed questionnaires regarding sociodemographic features and cisplatin-related AHOs [hearing impairment, tinnitus, peripheral sensory neuropathy (PSN), and kidney disease]. A CBMPt score encompassed the number and severity of cisplatin-related AHOs. Multivariable logistic regression models assessed the relationship of individual AHOs and CBMPt with medication use for anxiety and/or depression. Results: A total of 151 TCS (8.4%) used medications for anxiety and/or depression. No cisplatin-related AHOs were reported by 511 (28.4%) participants, whereas 622 (34.5%), 334 (18.5%), 287 (15.9%), and 48 (2.7%), respectively, had very low, low, medium, and high CBMPt scores. In the multivariable model, higher CBMPt scores were significantly associated with medication use for anxiety and/or depression (P < 0.0001). In addition, tinnitus (P = 0.0009), PSN (P = 0.02), and having health insurance (P = 0.05) were significantly associated with greater use of these medications, whereas being employed (P = 0.0005) and vigorous physical activity (P = 0.01) were significantly associated with diminished use. Conclusions: TCS with higher CBMPt scores had a higher probability of using medications for anxiety and/or depression, and conversely, those who were employed and physically active tended to have reduced use of these medications. Impact: Healthcare providers should encourage TCS to increase physical activity to improve both physical and mental health. Rehabilitation programs should assess work-related skills and provide career development counseling/training.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".