A Strong Correlation Between the Severity of Flatfoot and Symptoms of Knee Osteoarthritis in 95 Patients
Bibliographic record
Abstract
Objective The purpose of this study is to assess the association between the presence and severity of flatfoot and symptoms of knee OA. Methods 95 participants with knee OA were recruited from a patient cohort at a regional hospital. Symptoms of knee OA, including knee degeneration, femorotibial alignment, pain, stiffness and dysfunction were assessed using the Kellgren-Lawrence (K-L) grading system, femoral-tibial angle (FTA), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Participants were divided into groups with flatfoot (mild, moderate and severe) and without flatfoot based on the Clarke's angle. Linear regression and ordinal logistic regression were used for statistical analysis, as appropriate. Results Having flatfoot was associated with a significantly increased risk of having a higher K-L grade (OR: 20.03; 95% CI, 5.88, 68.27; p < 0.001), smaller FTA (Beta: −2.96; 95% CI, −4.41, −1.50; p < 0.001), higher pain score (Beta: 0.47; 95% CI, 0.24, 0.69; p < 0.001) and greater loss of function (Beta: 0.25; 95% CI, 0.02, 0.48; p = 0.03). Severe grades of flat feet were associated with a higher K-L grade (OR: 0.19; 95% CI, 0.08, 0.44; p < 0.001), smaller FTA (Beta: 1.51; 95% CI, 0.66, 2.35; p = 0.001), higher pain score (Beta: −0.25; 95% CI, −0.39, −0.11; p = 0.001), greater stiffness (Beta: −0.24; 95% CI, −0.38, −0.09; p = 0.002) and greater loss of function (Beta: −0.27; 95% CI, −0.41, −0.14; p < 0.001). Conclusion The results indicated that the severity of flattening is significantly associated with symptoms of knee OA. For the conservative management of knee OA, both flatfoot and its severity should be carefully considered.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".