Gait Metric Profile and Gender Differences in Hip Osteoarthritis Patients. A Case-Controlled Study
Bibliographic record
Abstract
PURPOSE: Hip osteoarthritis (OA) is a slowly progressive destructive disease that results in alterations in joint loads and biomechanics to which patients adapt compensatory alterations and abnormal gait patterns. This prospective cross-sectional, case-controlled study examined these alterations in gait metrics and evaluated gender differences in gait spatiotemporal parameters. Correlations between function and gait metrics were also investigated. BASIC PROCEDURES: Hip OA patients (138 females and 122 males) and healthy controls (14 females and 26 males) matched for age and gender underwent the same investigative protocol consisting of a spatiotemporal gait analysis followed by functional evaluations using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the SF-36 Health Survey (SF-36). MAIN FINDINGS: Differences between the patient and the control groups were significant in all the spatiotemporal parameters. There were significant gender differences within the hip OA group in all parameters except for cadence and single limb support percentage. WOMAC and SF-36 scores revealed significant differences between the study and control groups in most components. Significantly higher scores in the three components of the WOMAC as well as in six SF-36 score components were found among males compared to females in the patient group. PRINCIPAL CONCLUSIONS: Gait, WOMAC and SF-36 were effective objective and subjective tools for evaluating a large cohort of patients with hip OA, and can be highly useful for supplementing the assessment of hip OA severity and enhancing treatment efficacy during the course of the disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".