Impact of exercise on physical health status in bladder cancer patients
Bibliographic record
Abstract
INTRODUCTION: There is a scarcity of data on the impact of behavioral habits, such as exercise, on physical health in patients with bladder cancer. We investigated the association of exercise on self-reported physical health status and examined the prevalence of bladder cancer patients with sedentary lifestyle. METHODS: We examined cross-sectional data of participants diagnosed with bladder cancer within the Behavioral Risk Factor Surveillance System (BRFSS) from 2016-2020. Patient health status was surveyed using self-reported measures, such as the total days per month when their "physical health is not good." The primary outcome was patient-reported poor physical health for more than 14 days within a one-month period. RESULTS: Out of 2 193 981 survey participants, we identified 936 with a history of bladder cancer. Nearly one in three bladder cancer patients reported being sedentary within the last month, as a total of 307 (32.8%) patients reported no exercise within the last 30 days. The remaining 628 (67.2%) reported exercising for at least one day within the last month. In multivariable logistic regression model analysis, we found that exercise is protective for self-reported poor physical health status (odds ratio 0.37, 95% confidence interval 0.25-0.56, p<0.001). Patients that exercised were less likely to report bad physical health. CONCLUSIONS: Approximately one in three bladder cancer patients report no exercise within 30 days, suggesting a sedentary lifestyle. Patients that are active are less likely to self-report poor physical health status. Implementation of exercise programs for bladder cancer patients could be promising in improving health status.
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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 | 0.001 |
| 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".