Association of Varus Knee Thrust During Walking With Worsening Western Ontario and McMaster Universities Osteoarthritis Index Knee Pain: A Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To investigate the 2-year association of varus knee thrust observed during walking to the odds of worsening Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) knee pain in older adults with or at risk of osteoarthritis (OA). METHODS: Video recordings of self-paced walking trials of Multicenter Osteoarthritis Study participants were assessed for the presence of varus thrust at baseline. Knee pain was assessed using the WOMAC questionnaire at baseline and at 2 years. Logistic regression was used to estimate the odds of worsening knee pain (defined as either any increase in WOMAC score or as clinically important worsening), adjusting for age, sex, race, body mass index, clinic site, gait speed, and static knee alignment. Analyses were repeated, stratified by baseline radiographic OA status and among the subset of knees without baseline WOMAC pain. RESULTS: A total of 1,623 participants contributed 3,204 knees. Varus thrust was observed in 31.5% of knees. Knees with varus thrust had 1.44 times (95% confidence interval [95% CI] 1.19-1.73) the odds of any worsening and 1.37 times (95% CI 1.11-1.69) the odds of clinically important worsening WOMAC pain compared to knees without thrust. Knees with thrust without baseline WOMAC pain had 2.01 times (95% CI 1.47-2.74) the odds of incident total pain. CONCLUSION: Results indicate that varus thrust is a risk factor for worsening and incident knee pain. Targeting varus thrust through noninvasive therapies could prevent development or worsening of knee pain in older adults with or at risk for knee OA.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".