Does the 1-year Decline in Walking Speed Predict Mortality Risk Beyond Current Walking Speed in Adults With Knee Osteoarthritis?
Bibliographic record
Abstract
OBJECTIVE: To investigate whether walking speed at 1 timepoint, decline over the past 12 months, or both predict mortality risk over 11 years in adults with, or at risk of, knee osteoarthritis (OA). METHODS: Using the data from the Osteoarthritis Initiative, we defined slow versus adequate walking speed as walking < 1.22 versus ≥ 1.22 m/s on a 20m walk test during the 12-month follow-up visit. We defined meaningful decline (yes/no) as slowing ≥ 0.08 m/s over the past year. At the 12-month visit, we classified adequate sustainers as those with adequate walking speed and no meaningful decline, slow sustainers as slow walking speed and no meaningful decline, adequate decliners as adequate walking speed and meaningful decline, and slow decliners as slow walking speed and meaningful decline. Mortality was recorded over 11 years. To examine the association of walking speed with mortality, HR and 95% CI were calculated using Cox regression, adjusted for potential confounders. RESULTS: ), 6% (n = 270) died over 11 years. Slow sustainers and slow decliners had 2-times increased mortality risk compared to adequate sustainers (HR 1.96, 95% CI 1.44-2.66 for slow sustainers, and HR 2.08, 95% CI 1.46-2.96 for slow decliners). Adequate decliners had 0.43 times the mortality risk compared with adequate sustainers (HR 0.57, 95% CI 0.32-1.01). CONCLUSION: In adults with, or at risk of, knee OA, walking slower than 1.22 m/s in the present increased mortality risk, regardless of decline over the previous year.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".