The Influence of Body Mass Index, Q-angle and Tibiofemoral Alignment on the Clinical Deficits of Osteoarthritis of the Knee
Bibliographic record
Abstract
Background: The underlying determinants of clinical deficits of knee OA remain crucial during objective assessment, which is yet to receive the deserved attention in practice. Objective: The study determined the influence of BMI, Q-angle and Tibiofemoral alignment (TFA) on pain, stiffness and physical function of patients with knee OA. Methods: Patients diagnosed with knee OA were recruited at physiotherapy departments of three health care settings in Ghana. The BMI of participants was determined through the standard formula [weight (kg)/height (m) 2 ]. The measurement of Q-angle and TFA followed routine manual methods with the use of goniometer and tracing sheet. To quantify pain intensity, joint stiffness and physical function of participants, the domains of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was employed. Spearman’s correlation coefficient was used to determine the relationships between variables at p<0.05. Results: Fifty-two (52) patients with knee OA (Mean age; 60.2±10.4years) participated in the study. They comprised 9 (17.3%) males and 43 (82.7%) females. Participants’ BMI and Q angle were significantly and positively correlated with their physical functioning (rho=0.368; p=0.007) and (rho=0.332; p=0.016) respectively on the domains of WOMAC. However, participants’ self-reported pain and stiffness as assessed on WOMAC index were not significantly correlated with BMI, Q angle and clinical TFA. Conclusion: BMI and Q-angle could considerably influence the overall clinical deficits in patients with knee OA particularly the physical function, thus placing emphasis on routine assessment of these variables in the clinical practice.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".