Management of Chronic Lateral Epicondylitis With Manual Therapy and Local Cryostimulation: A Pilot Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this pilot study was to evaluate the feasibility and efficacy of adding cryostimulation to manual therapy in patients with chronic lateral epicondylitis. METHODS: The control group (n = 19) was treated with manual therapy consisting of soft-tissue therapy and radial head mobilizations. The experimental group (n = 18) received cryostimulation in addition to manual therapy care similar to that for the control group. Both protocols consisted of 8 treatments over a 4-week period. Outcome measures included pain intensity (visual analog scale), pain-free grip strength (handheld dynamometer), and functional index (Patient-Rated Tennis Elbow Evaluation questionnaire). Assessments were performed at baseline, postintervention, and 3-month follow-up. Adherence and dropout rates were also considered. RESULTS: Both groups exhibited significant improvements in pain intensity and functional index at postintervention assessments, which were maintained at follow-up. All participants attended the prescribed number of treatments, but 27% were lost at follow-up. Minor adverse events were reported after cryostimulation in 4 cases. CONCLUSIONS: This study indicated that it is feasible to complete a clinical trial evaluating the efficacy of adding cryostimulation to manual therapy in patients with chronic lateral epicondylitis. On the basis of these preliminary data, the combination of cryostimulation and manual therapy care did not provide any additional benefits in both the short term and the long term. Manual myofascial point treatment and mobilization techniques yielded positive outcomes in chronic lateral epicondylitis. Further studies should focus on the sole therapeutic effect of cryostimulation in both patients with acute and those with chronic conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".