AQUATIC SPORTS REHABILITATION ON FUNCTIONAL RECOVERY OF KNEE JOINT INJURY
Bibliographic record
Abstract
ABSTRACT Introduction Knee joint injury is a common sports injury and how to speed up the recovery process is a concern for all athletes. Objective Determine whether aquatic sports rehabilitation nursing can accelerate the rehabilitation process of knee joint injuries. Methods Thirty patients with knee ligament or meniscal injury were divided into an aquatic sports group and a control group. The control group assumed general recovery measures, while the aquatic exercise group was added another 8 weeks of rehabilitation nursing with aquatic exercises. The evolution of the degree of knee joint pain, knee joint range of motion, knee joint muscle strength and other indicators before and after the experiment were compared. Results After 8 weeks of intervention, pain and activities of daily living in the aquatic exercise group improved significantly compared with those before the experiment; the degree of recovery from knee joint injury in the aquatic exercise group was significantly better than that in the control group, and knee joint flexion range of motion, flexor and extensor muscle strength, and other indicators in the aquatic exercise group were significantly better than those in the control group. Conclusion Aquatic rehabilitation exercise can accelerate the recovery process of patients with knee joint injuries. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.005 |
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".