Comparative Effectiveness of Gua Sha, Cryostretch, and Positional Release Technique on Tenderness and Function in Subjects with Plantar Fasciitis: a Randomized Clinical Trial
Bibliographic record
Abstract
Background: Plantar fasciitis (PF) can be treated effectively with manual techniques like cryostretch (CS) and the positional release technique (PRT). Although Gua Sha (GS) has been suggested in the literature for PF, its efficacy has not been studied in the research. Objective: To determine and compare the effectiveness of GS, CS, and PRT in subjects with PF in terms of pain intensity, pain pressure threshold, and foot function. Methods/Design: Thirty-six patients with PF (n=36) were randomly allocated to three study groups (12 in each group)-group GS, group CS, and group PRT, respectively. Settings: A randomized clinical trial was conducted at physiotherapy OPD in a tertiary health center. Participants: Subjects of all genders with plantar fasciitis of the age group 20-60 years. Thirty-six subjects with plantar fasciitis out of whom 12 were males and 24 females. There were no dropouts in this study. Intervention: The interventions included the Gua Sha technique (1 session), the cryostretch technique with a frozen tennis ball (3 sessions), and the positional release technique (7 sessions), along with common exercises for all three groups. Outcome Measures: Pain intensity, foot functions, and pain pressure threshold were assessed using the Numerical Pain Rating Scale, Foot Function Index, and pressure algometer, respectively, on day 1 (pre-intervention) and day 7 (post-intervention). Results: =.0001). Conclusion: Although all three groups showed improvement, Gua Sha was superior in terms of reducing pain, cryostretch for improving foot functions, and PRT for reducing tenderness. The interventions used in this study are cost-effective and have proved to be simple and safe techniques.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".