A Decline in Walking Speed Is Associated With Incident Knee Replacement in Adults With and at Risk for Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To determine if a 1-year change in walking speed is associated with receiving an incident knee replacement during the following year in adults with and at risk for knee osteoarthritis (OA). METHODS: Using data from the Osteoarthritis Initiative, we determined a 1-year change in the 20-meter walk speed from 3 observation periods (i.e., 0-12, 12-24, and 24-36 months). We operationally defined 1-year change in walking speed as either (1) decline: ≤ -0.1 m/s change, (2) no change: between -0.1 and 0.1 m/s change, and (3) increase: ≥ 0.1 m/s change. Incident knee replacement was defined using each subsequent 1-year period (i.e., 12-24, 24-36, and 36-48 months). Combining data from the 3 observation periods, we performed a Poisson regression with robust error variance to determine the relative risk between a change in walking speed (exposure) and incident knee replacement over the following year (outcome). RESULTS: Of the 4264 participants included within this analysis (11,311 total person visits), 115 (3%) adults received a knee replacement. Decline in walking speed was associated with a 104% increase in risk [adjusted relative risk (RR) 2.04, 95% CI 1.40-2.98], while an increase in walking speed associated with a 55% decrease in risk (RR 0.45; 95% CI 0.22-0.93) of incident knee replacement in the following year compared to a person with no change in walking speed. CONCLUSION: A 1-year decline in walking speed is associated with an increased risk, while a 1-year increase in walking speed is associated with a decreased risk of future incident knee replacement.
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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 | 0.006 |
| 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 | 0.000 |
| 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".