Association of physical activity with overall mortality among long‐term testicular cancer survivors: A longitudinal study
Bibliographic record
Abstract
Physical activity (PA) has been associated with reduced mortality among cancer survivors, but no study has focused on testicular cancer survivors (TCSs). We aimed to investigate the association of PA measured twice during survivorship with overall mortality in TCSs. TCSs treated during 1980 to 1994 participated in a nationwide longitudinal survey between 1998 to 2002 (S1: n = 1392) and 2007 to 2009 (S2: n = 1011). PA was self-reported by asking for the average hours per week of leisure-time PA in the past year. Responses were converted into metabolic equivalent task hours/week (MET-h/wk) and participants were categorized into: Inactives (0 MET-h/wk), Low-Actives (2-6 MET-h/wk), Actives (10-18 MET-h/wk) and High-Actives (20-48 MET-h/wk). Mortality from S1 and S2, respectively, was analyzed using the Kaplan-Meier estimator and Cox proportional hazards models until the End of Study (December 31, 2020). Mean age at S1 was 45 years (SD 10.2). Nineteen percent (n = 268) of TCSs died between S1 and EoS, with 138 dying after S2. Compared to Inactives at S1, the mortality risk among Actives was 51% lower (HR 0.49, 95% CI: 0.29-0.84) with no further mortality reduction among High-Actives. At S2, the mortality risk was at least 60% lower among the Actives, High-Actives and even the Low-Actives compared to the Inactives. Persistent Actives (≥10 MET-h/wk at S1 and S2) had a 51% lower mortality risk compared to Persistent Inactives (<10 MET-h/wk at S1 and S2; HR 0.49, 95% CI: 0.30-0.82). During long-term survivorship after TC treatment, regular and maintained PA were associated with an overall mortality risk reduction of at least 50%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".