Biomechanics-based optimization of knee joint rehabilitation technology and its integration with the health industry: Enhancing medical service efficiency and health economic value
Bibliographic record
Abstract
Background: Knee osteoarthritis (KOA) is highly prevalent among the elderly population, with traditional treatments focusing primarily on medication or surgery, while precise rehabilitation and health economic evaluations remain insufficient. Biomechanics-oriented rehabilitation interventions may offer higher efficiency and safety. Objective: To explore the clinical efficacy, equipment performance, and cost-effectiveness of a novel rehabilitation training system based on biomechanical analysis for KOA patients and to verify the correlation between changes in joint torque and functional improvement. Methods: A total of 80 KOA patients were enrolled and randomly assigned in a 1:1 ratio into the intervention group and the control group, with 40 cases in each group. The intervention group utilized a novel rehabilitation training system incorporating biomechanical analysis, while the control group used conventional mechanical equipment. Three-dimensional gait parameters (e.g., peak joint angle, peak torque, loading rate) were measured at baseline, 6 weeks, and 12 weeks post-intervention. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores and equipment performance indicators were assessed, while total treatment costs and cost-benefit ratios were calculated. The intervention effects were evaluated using independent sample t-tests, chi-square tests, and Pearson correlation analysis. Results: The intervention group showed significant improvements in peak joint angles, peak torque, and loading rates compared to baseline (p < 0.05), while soft tissue pressure did not increase significantly (p > 0.05). The novel equipment demonstrated significantly better performance in terms of angle and torque detection errors compared to conventional equipment (p < 0.05). The intervention group had lower total treatment costs and a superior cost-benefit ratio (p < 0.01), with no statistically significant difference in adverse event incidence (p > 0.05). Gait trajectory improvements were significant at multiple time points (p < 0.05), and clinical function (WOMAC score, walking distance) and healthcare efficiency also improved (p < 0.05). Changes in joint torque were strongly correlated with WOMAC score improvement (r = 0.628, p < 0.001). Conclusion: The biomechanics-driven rehabilitation training system significantly enhances clinical efficacy, equipment performance, and economic burden management, achieving precise rehabilitation and resource optimization, with demonstrable application value in the health industry.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".