Cryotherapy for the Management of Chronic Musculoskeletal Conditions: A Clinical Assessment of Pain and Function
Bibliographic record
Abstract
Background: Chronic pain, defined as pain persisting or recurring for more than three months, affects an estimated 13.5% to 47% of the general population, with chronic musculoskeletal pain prevalence ranging from 11.4% to 24%. Given the high and rising prevalence of musculoskeletal pain, driven by demographic trends, there is an urgent need for effective interventions, particularly in rehabilitation care settings. Materials and Methods: Twenty patients with chronic musculoskeletal conditions participated in a treatment program consisting of 10 cold-air cryotherapy sessions. The effectiveness of the therapy was evaluated by assessing changes in pain, range of motion, and physical function using the Western Ontario and McMaster Universities Arthritis Index (WOMAC) before and after treatment. Results: All assessed parameters showed statistically significant improvements. Pain, measured using the Visual Analog Scale, decreased by 42.9%, while range of motion increased by 15.23%. The WOMAC questionnaire indicated improvements of 34.68% in pain, 40.26% in stiffness, and 24.87% in physical function. Conclusions: Cold-air cryotherapy demonstrates potential as an effective treatment for various chronic musculoskeletal conditions. Its efficacy, ease of application, affordability, and transportability make it a promising alternative not only for acute edematous injuries but also for chronic pain management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".