Deep Front Line Myofascial Release Versus Novel Soft Tissue Kinetic Chain Activation Technique (K-CAT) on Pain, Radiological Patellar Position and Dynamic Knee Valgus in Knee Osteoarthritis: A Randomized Clinical Trial
Bibliographic record
Abstract
Background: Knee osteoarthritis (OA) is the most common degenerative condition, afflicting large number of people globally. Fascia is a three-dimensional network of connective tissue that helps in force transmission along the myofascial chains to bone level causing malalignments and movement dysfunctions. Myofascial dysfunctions have been identified in osteoarthritis of knee as a pain-causing component. Recently, clinicians have aimed a variety of therapeutic techniques at fascia. There is a lack of literature to determine the effect of kinetic chain activation technique (K-CAT) as well as deep front line (DFL) release technique in OA knee. Purpose: The current study aimed to determine and compare the effectiveness of DFL release and K-CAT in knee OA. Methods: The study was a randomized clinical trial conducted in an outpatient department of a tertiary care hospital. Thirty-two (n = 32) participants between 45 and 60 years of age with knee osteoarthritis (grades 2 and 3) were included and randomized into two groups based on selection criteria. Group A received DFL myofascial release and Group B received K-CAT, along with common conventional therapy (modality + exercises), three sessions per week for 2 weeks. Pain intensity using Numeric Pain Rating Scale, skyline view of knee radiographic parameters including lateral patellar tilt angle (LPTA) and bisect offset (BO), dynamic knee valgus (DKV) by single leg squat using Kinovea software and quality of life using Knee Injury and Osteoarthritis Outcome Score on day 1 and day 14 of intervention were assessed. Results: Within-group analyses showed significant improvements in both the groups for pain, BO on x-ray, DKV, and Knee Injury and Osteoarthritis Outcome Score (p < 0.05). LPTA showed statistical significance only in the DFL group. However, between-group comparisons showed no statistical difference in all the outcomes (p > 0.05). Conclusion: Both DFL myofascial release and K-CAT were found to be equally effective in alleviating pain, improving quality of life and knee malalignments.Trial registered under Clinical Trial Registry of India (CTRI/2023/11/059388).
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".