A Strong Correlation Between the Severity of Flatfoot and Symptoms of Knee Osteoarthritis in 95 Patients
Bibliographic record
Abstract
Abstract Objective: The purpose of this study was to assess the association between the flat feet and the symptoms of knee osteoarthritis (OA). Methods: 95 participants with knee osteoarthritis were recruited for this study. Knee osteoarthritis was evaluated using a combination of Kellgren-Lawrence (K-L) grade, femoral-tibial angle (FTA), knee pain and physical function. The Arch Index (AI) was used to assess the severity of flat feet, with subjects being divided into groups with no flat feet, mild flat feet and severe flat feet. A chi-square test, continuous variable t test and Mann-Whitney U test were used to compare differences in K-L grade, knee pain, physical function and femoral-tibial angle. Regression analysis was performed to assess the correlation between the flat feet and the characteristics of knee osteoarthritis. Results: Flat feet was associated with a significantly increased risk of having a larger varus angle (beta: -2.306; 95% confidence interval (CI): -3.703, -0.909; p = 0.001), higher pain score (beta: 0.356; 95% CI: 0.130, 0.582; p = 0.002) and greater loss of physical function (beta: 0.281; 95% CI: 0.071, 0.491; p = 0.009). Severe grades of flat feet were associated with an increased risk of pain (beta: -0.289; 95% CI: -0.543, -0.035; p = 0.026). However, the severity of flat feet was not associated with K-L grade, FTA or physical function. Conclusion: There was a significant association between the presence of flat feet and symptoms of knee OA, and severe flat feet was found to aggravate OA-related knee pain. Active treatment to correct flat feet is recommended to relieve symptoms associated with knee OA. Level of Evidence: Level II
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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".