Risk of Knee Osteoarthritis Over 24 Months in Individuals Who Decrease Walking Speed During a 12-Month Period: Data from the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: To assess the association between change in walking speed over a 12-month period and risk of developing radiographic knee osteoarthritis (rKOA) over a 24-month period. METHODS: We included participants without rKOA from the Osteoarthritis Initiative. Change in walking speed was determined from a 20-m walk assessment, calculated using walking speed at 12-month followup minus baseline speed and/or 24-month followup walking speed minus 12-month speed. Incident rKOA was defined as progressing to Kellgren-Lawrence arthritis grading scale ≥ 2 within 24 months (i.e., incidence between 12 and 36 mos or 24 and 48 mos). Self-reported significant knee injury during the exposure period, age, body mass index (BMI), and Physical Activity Scale for the Elderly (PASE) score were adjusted for analytically. RESULTS: We included 2638 observations among 1460 unique participants (58% women; aged 59 ± 9 yrs, range 45-79). The mean change in walking speed over 12 months was 0.001 ± 0.13 m/s (range -0.6271 to 1.4968). About 5% of the sample (n = 122) developed rKOA over a 24-month period. After controlling for significant knee injury, age, BMI, and PASE score, we found an 8% relative increase in risk of developing rKOA for every 0.1 m/s decrease in walking speed over a 12-month period (risk ratio 1.08, 95% CI 1.00-1.15, p = 0.05). CONCLUSION: Evaluating change in speed over a 12-month period using a 20-m walk test may be useful in identifying individuals at increased risk of developing rKOA over the subsequent 24 months. Identification of patients at high risk for developing rKOA would allow medical providers to implement early interventions to maximize joint health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".