Cognitive function in long-term testicular cancer survivors: impact of modifiable factors
Bibliographic record
Abstract
No study has comprehensively examined associated factors (adverse health outcomes, health behaviors, and demographics) affecting cognitive function in long-term testicular cancer survivors (TC survivors). TC survivors given cisplatin-based chemotherapy completed comprehensive, validated surveys, including those that assessed cognition. Medical record abstraction provided cancer and treatment history. Multivariable logistic regression examined relationships between potential associated factors and cognitive impairment. Among 678 TC survivors (median age = 46; interquartile range [IQR] = 38-54); median time since chemotherapy = 10.9 years, IQR = 7.9-15.9), 13.7% reported cognitive dysfunction. Hearing loss (odds ratio [OR] = 2.02; P = .040), neuropathic pain (OR = 2.06; P = .028), fatigue (OR = 6.11; P < .001), and anxiety/depression (OR = 1.96; P = .029) were associated with cognitive impairment in multivariable analyses. Being on disability (OR = 9.57; P = .002) or retired (OR = 3.64; P = .029) were also associated with cognitive decline. Factors associated with impaired cognition identify TC survivors requiring closer monitoring, counseling, and focused interventions. Hearing loss, neuropathic pain, fatigue, and anxiety/depression constitute potential targets for prevention or reduction of cognitive impairment in long-term TC survivors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".