Disease severity and knee extensor force in knee osteoarthritis: Data from the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: To determine whether the method of disease severity measurement influences the magnitude of knee extensor force deficits in knee osteoarthritis (OA). METHODS: Data from the Osteoarthritis Initiative (n = 659) were analyzed. Knee extensor force was assessed with isometric contractions. Clinical severity was measured with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Patients were stratified into tertiles of severity (i.e., moderate, mild, and severe OA) based on the lowest, middle, and highest WOMAC scores, respectively. Kellgren/Lawrence (K/L) grading was used to assess radiographic severity of the tibiofemoral compartment and patients were again stratified into mild (K/L grade <2), moderate (K/L grade 2), and severe (K/L grade >2) knee OA. RESULTS: When stratifying with the WOMAC, force was significantly lower in the severe group compared to the mild (~18% lower; P < 0.001) and moderate groups (~9% lower; P = 0.03), and in the moderate group compared to the mild group (∼10% lower; P = 0.03). When stratifying with K/L grade, small nonsignificant differences were observed in the severe (~7% lower; P = 0.19) and moderate groups (~8% lower; P = 0.08) compared to the mild group. Large intragroup variability was observed when comparing WOMAC scores across radiographic severity (coefficients of variation were 79.3%, 74.6%, and 61.6% for K/L grade <2, K/L grade 2, and K/L grade >2, respectively). CONCLUSION: The method of disease severity stratification influences the magnitude of knee extensor force deficits because no difference in force between disease subgroups was observed when stratifying with K/L grade. Furthermore, there was large variability in the WOMAC score within each radiographic subgroup, highlighting the limitations in using radiographic measures to reflect symptom severity.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".