Relationship of Cisplatin-Related Adverse Health Outcomes With Disability and Unemployment Among Testicular Cancer Survivors
Bibliographic record
Abstract
Abstract Background Few data exist on the relationship of cisplatin-related adverse health outcomes (AHOs) with disability, unemployment, and self-reported health (SRH) among testicular cancer survivors (TCS). Methods A total of 1815 TCS at least 1 year postchemotherapy underwent clinical examination and completed questionnaires. Treatment data were abstracted from medical records. A cumulative burden of morbidity score (CBMPt) encompassed the number and severity of platinum-related AHOs (peripheral sensory neuropathy [PSN], hearing loss, tinnitus, renal disease). Multivariable regression assessed the association of AHOs and CBMPt with employment status and SRH, adjusting for sociodemographic and clinical characteristics. Unemployment was compared with a male normative population of similar age, race, and ethnicity. Results Almost 1 in 10 TCS was out of work (2.4%, disability leave; 6.8%, unemployed) at a median age of 37 years (median follow-up = 4 years). PSN (odds ratio [OR] = 2.89, 95% confidence interval [CI] = 1.01 to 8.26, grade 3 vs 0, P = .048), renal dysfunction defined by estimated glomerular filtration rate (OR = 12.1, 95% CI = 2.06 to 70.8, grade 2 vs 0, P = .01), pain (OR = 10.6, 95% CI = 4.40 to 25.40, grade 2 or 3 vs 0, P < .001), and CBMPt (OR = 1.46, 95% CI = 1.03 to 2.08, P = .03) were associated with disability leave; pain strongly correlated with PSN (r2 = 0.40, P < .001). Statistically significantly higher percentages of TCS were unemployed vs population norms (age-adjusted OR = 2.67, 95% CI = 2.49 to 3.02, P < .001). PSN (OR = 2.44, 95% CI = 1.28 to 4.62, grade 3 vs 0, P = .006), patient-reported hearing loss (OR = 1.82, 95% CI = 1.04 to 3.17, grade 2 or 3 vs 0, P = .04), and pain (OR = 3.75, 95% CI = 2.06 to 6.81, grade 2 or 3 vs 0, P < .001) were associated with unemployment. Increasing severity of most cisplatin-related AHOs and pain were associated with statistically significantly worse SRH. Conclusions Our findings have important implications regarding treatment-associated productivity losses and socioeconomic costs in this young population. Survivorship care strategies should include inquiries about disability and unemployment status, with efforts made to assist affected TCS in returning to the workforce.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".