The Effect of TRX vs. Aquatic Exercises on Self-Reported Knee Instability and Affected Factors in Women with Knee Osteoarthritis: A Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Background Knee Instability (KI) is described as a sense of the knee buckling, shifting, or giving way during the weight bearing activities. High prevalence (65%) has been reported for KI amongst the patients with knee osteoarthritis (KOA). So, we studied the effect of two interventions on self-reported KI and affected factors.Methods In this single blind, randomized, and controlled trial, 36 patients with radiographic grading (Kellgren–Lawrence 1–4) of knee osteoarthritis were selected for participating. patients were allocated in three groups aquatic exercises (n=12), Total Resistance eXercises (TRX) exercises (n=12) and control (n=12) by random. 8-week TRX and aquatic exercises were carried out by experimental groups. Pain severity was assessed by visual analog scale (VAS), Balance was also evaluated by Berg Balance Scale (BBS), quadriceps strength by dynamometer, and knee range of motion (ROM) by inclinometer, Western Ontario and McMaster Universities Osteoarthritis (WOMAC), self-reported KI were also measured before and after interventions.Results The results of One-way ANOVA showed that there was no significant difference between aquatic exercises and TRX (P>0.05) for KI, BBS, WOMAC, and pain. But there was significant difference between the aquatic exercises and the control for KI (P=0.0001), BBS (P=0.0001), WOMAC Stiffness (P=0.0001), and pain (P=0.006). Also, there was significant difference between the TRX and the control for KI (P=0.0001), BBS (P=0.0001), and pain (P=0.003) except WOMAC Stiffness (P=0.07).Conclusions TRX and aquatic interventions had a similar effect on the patients’ KI, pain, function, and also balance variables, but TRX exercises had more effect on the knee stiffness improvement.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".