Psychotropic and stimulant medication (PSM) use among testicular cancer survivors (TCS): A multi-institutional clinical study of 680 patients given cisplatin-based chemotherapy (CHEM) (NCI 1R01 CA157823-02).
Bibliographic record
Abstract
242 Background: Testicular cancer (TC) is the most common cancer in men aged 15-40, with survival rates after diagnosis > 95%. Testicular cancer survivors (TCS) are known to be at increased risk for certain acute and chronic medical conditions, but few studies have examined barometers of their psychological health. The objective is to characterize the prevalence of PSM use and associations with demographics, health behaviors, and treatment-associated toxicities among TCS. Methods: TCS aged < 50 years at first-line CHEM completed a questionnaire regarding co-morbidities and prescription drug use, including PSMs. For co-morbidities, peripheral neuropathy (PN) responses of ‘a little’, “quite a bit”, or “very much” were scored ‘yes.’ Fisher’s exact test was used to examine the significance of various associations. Results: Among the first 680 consecutively enrolled TCS, median age at TC diagnosis was 31y (range, 15-49y) and median time since CHEM completion was 52mo (range 12-360mo). 85 TCS (12.5%) reported PSM use, including antidepressants (N = 65 [76.5%]), anxiolytics (N = 23 [27%]), and stimulants (N = 21 [25%]) with 20 TCS on ≥ 2 PSMs (23%). Compared to non-users, more PSM users were unemployed (11.8% vs. 4.4%%; P < .01), self-rated their health as fair/poor (12.2% vs 4%; P < .01), and had gained > 20lb since CHEM (39.8% vs 23.4%;P < .01). PSM users were more likely to have tinnitus (49.4% vs. 36.4%; P < 0.04), both tinnitus and PN (43.5% vs. 27.2%; P < 0.01), cardiovascular disease (26.2% vs. 15.6%; P < .02), and greater use of prescription medications for pain control (20% vs. 4.7%; P < 0.01), hypertension (16.5% vs. 7.1%; P < 0.01), diabetes (8.3% vs. 2.9%; P < 0.02), and testosterone replacement (10.6% vs. 5.0%; P = 0.048). Conclusions: Future studies should aim for identification of high-risk patients in need of intensified preventive and therapeutic interventions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".