Usual walking Pace and risk of 28 cancers– results from the UK biobank
Bibliographic record
Abstract
BACKGROUND: Usual walking pace represents a practical indicator of overall health. However, its association with cancer development remains unexplored. We investigated the relation between self-reported walking pace and cancer risk. METHODS: Using baseline UK Biobank data from 2006 to 2010, excluding the first two years of follow-up to reduce reverse causation, we employed multivariable Cox regression to assess the association between walking pace (slow, steady average, brisk) and risk of 28 cancer types, accounting for overall physical activity and walking volume. RESULTS: After a median follow-up of 10.9 years (interquartile range 10.1-11.8), 8.3% of 334,924 participants received a cancer diagnosis. Brisk compared to slow walking pace was associated with multivariable-adjusted lower risks of five cancers, including anal (hazard ratio 0.30; 95% confidence interval: 0.14-0.63), hepatocellular carcinoma (0.39; 0.23-0.66), small intestine (0.46; 0.24-0.87), thyroid (0.50; 0.29-0.86), and lung cancer (0.60; 0.51-0.70). Our findings were consistent across various sensitivity analyses, which assessed sex and age differences, residual confounding, and reverse causation. CONCLUSIONS: Self-reported walking pace was inversely associated with risk of five cancer types, even when accounting for overall physical activity and walking volume. Adopting a brisk walking pace may represent a pragmatic target for public health interventions to decrease cancer risk, particularly in circumstances where increases in walking volume or frequency prove impractical.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".