[Analysis of kinematic changes of hip joint after total hip arthroplasty].
Bibliographic record
Abstract
Objective: To analyze the kinematic changes of the hip joint after total hip arthroplasty (THA) through three-dimensional gait analysis. Methods: Patients with hip joint diseases admitted between October 2022 and June 2023 were selected as the subjects. The patients who met the selective criteria were finally included in the THA group. The healthy volunteers matched with the THA group in the same age were included as the control group. Baseline data including age, gender, body mass index (BMI), and laterality were compared between the two groups. The Harris hip score (HHS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score were recorded preoperatively and at last follow-up in the THA group. Three-dimensional motion capture system was utilized to collect spatiotemporal parameters and kinematic data during walking, including stride length, cadence, and maximum/minimum values, range of motion (ROM) in hip joint abduction/adduction, external/internal rotation, and flexion/extension, as well as gait scores. Differences between the two groups were analyzed. Additionally, the correlation between gait scores and postoperative HHS and WOMAC scores were analyzed in the THA group. Finally, the kinematic data of each degree of freedom (DOF) were fitted into a gait diagram, and the dynamic changes of the 3-DOF of the hip joint during the gait cycle were quantitatively analyzed. Results: <0.05). Conclusion: Early post-THA hip joint kinematics exhibit relative adduction, external rotation, and flexion during the gait cycle compared to normal individuals, with incomplete recovery of kinematic parameters in three degrees of freedom. Significant differences in flexion are observed at multiple phases of the gait cycle compared to normal individuals.
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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 | 0.001 |
| 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".