Which Predicts Quadriceps Muscle Strength in Knee Osteoarthritis: Biological Markers or Clinical Variables?
Bibliographic record
Abstract
OBJECTIVES: This study aims to evaluate the relationship between biological markers and quadriceps muscle strength, the correlation of clinical variables with quadriceps muscle strength, and the results according to the radiological severity in patients with knee osteoarthritis. PATIENTS AND METHODS: A total of 152 patients (22 males, 130 females; mean age 57.3±7.5 years; range 40 to 70 years) with primary knee osteoarthritis were included in the study. We evaluated biological markers of C-telopeptide of type I collagen, C-telopeptide of type II collagen, leptin, and osteocalcin along with 25-hydroxy vitamin D. We measured quadriceps muscle strength both by manual muscle tester and computerized isokinetic dynamometer. We evaluated pain and functional status of the patients by visual analog scale and Western Ontario and McMaster Universities Osteoarthritis Index. We analyzed the correlation between biological markers and quadriceps muscle strength along with clinical variables. We classified the strength of correlations as no-very weak, weak-moderate, moderate-strong, and excellent. RESULTS: Of the patients, 76.9% (n=117) were obese. Quadriceps muscle strength measures were significantly lower in females than that in males. There was no-very weak correlation between biological marker levels and quadriceps muscle strength. However, weak-moderate correlations were found between clinical variables (pain and Western Ontario and McMaster Universities Osteoarthritis Index scores) and quadriceps muscle strength measures. CONCLUSION: Among the measured biological markers, none had any influence on quadriceps muscle strength in patients with knee osteoarthritis. However, pain and functional status of the patients might affect quadriceps muscle strength.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".