The Clinical Effect of Low-load Blood Flow Restriction Training Combined with Intra-articular Injection for Elderly Knee Osteoarthritis
Bibliographic record
Abstract
Objective To investigate the clinical effect of low-load blood flow restriction training combined with intra-articular injection for elderly knee osteoarthritis. Methods A total of 126 elderly patients with knee osteoarthritis admitted to the Third Hospital of Shanxi Medical University in 2023 were selected as the study subjects by convenient sampling method, they divided into two groups according to the random number table, 63 cases for each group. The control group received simple intra-articular injection, and the observation group was additionally trained with low-load blood flow restriction. Western Ontario and the University of Manchester Osteoarthritis Index (WOMAC), efficacy, generic quality of life inventory 74 (GQOLI-74) score, knee extension moment, knee adduction moment, serum matrix metalloproteinase (MMP) -1, and MMP-3 levels were assessed before and after treatment. Results After treatment, the WOMAC dimensions and total score, MMP-1 and MMP-3 levels in the observation group were lower than those in the control group (P < 0.05), and the clinical total effective rate, GQOLI-74 score dimensions and total score, knee extension moment and knee adduction moment in the observation group were higher than those in the control group (P < 0.05) . Conclusion Low-load blood flow restriction training combined with intra-articular injection for elderly knee osteoarthritis has good clinical efficacy, which can significantly relieve pain, improve joint function and quality of life of patients, and its treatment mechanism may be related to lower levels of MMP-1 and MMP-3.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".