The relationship of radiographic severity with BMI, pain, and physical function in elderly women with knee osteoarthritis: A cross-sectional study
Bibliographic record
Abstract
Abstract Objective: The aim of this study was to investigate the relationship between radiographic severity and BMI, pain, and physical function in elderly women with knee osteoarthritis (OA). Methods: A total of 80 elderly women with knee OA were enrolled in this study. Their age, course of disease and body mass index (BMI) were recorded. Radiographic severity was assessed with the Kellgren-Lawrence (K/L) scale, and pain, stiffness, and physical function were assessed with Western Ontario and McMaster University Osteoarthritis Index (WOMAC) scales, objective assessment of patients' functional performance using the Time Up and Go (TUG) test. Spearman correlation analysis was used to assess the relationship between radiographic K/L scores and BMI, pain, and physical function. Logistic regression analysis was used to determine the important factors contributing to knee OA severity. Result：The mean age of the patients was 64.7 ± 6.77 years, and the mean course of disease was 5.02±2.12 years. There was no significant correlation between radiographic severity K/L and normal BMI (p=0.087), but there was a moderate correlation between radiographic severity K/L and high BMI (p=0.02, r=0.457). It was positively correlated with WOMAC pain, stiffness and physical function (p=0.001, r=0.447; p=0.004, r=0.316; p=0.025, r=0.250). In addition, K/L grading was positively correlated with TUG (p= 0.001, r=0.395). On the other hand, BMI, pain, and stiffness may be the important influencing factors of the severity of knee OA, while age, course of disease, and physical function have little influence on the severity of knee OA. Conclusion: The radiographic severity of knee OA was correlated with high BMI, pain, and physical function, and high BMI, knee pain and stiffness were important factors for the severity of 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) | 0.000 | 0.000 |
| 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 | 0.000 | 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".