GAIT SPEED AND MORTALITY IN OLDER ADULTS: WHY TIMING MATTERS
Bibliographic record
Abstract
Recently, an emerging body of literature has indicated a strong association between poor gait speed and mortality. However, these studies have several methodological limitations. Based on secondary analysis of data from existing longitudinal studies of aging, they generally measure gait speed at a single time point and use this time point as the time origin in assessing the association between gait speed and survival over several years. The objective of this study is to estimate the association between gait speed and mortality, using a meaningful time axis, and accounting for the time-varying effects of other health characteristics. The study is based on data from the Cardiovascular Health Study, a study of 5,201 individuals aged 65 years and over, with annual measurements of gait speed and several covariates over a period of 10 years. Using age rather than time-on-study as the time-axis, I apply a time-varying Cox model to estimate the independent effects of gait speed on morality, while accounting for the effects of health characteristics, including depression, cognitive function, and chronic disease. For comparison, I provide estimates of models where variables are treated as time-fixed. Overall, I found that the time-varying measure of gait speed yields a stronger association with mortality compared to the time-fixed measure. Furthermore, the control for health and lifestyle factors attenuates the association in women, but not in men. Using time-varying measures of gait speed and controlling for health and lifestyle confounders provides a more meaningful estimate of the association between gait speed and mortality.
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".