Is the Duration of Spa Cure Treatment Important in Knee Osteoarthritis? A Randomized Controlled Study
Bibliographic record
Abstract
BACKGROUND: Spa therapies are applied in varying durations. Today, the duration of spa therapy may be long for people with active lives. How should we determine the duration of treatment in order to minimize therapy costs and loss of labor force? Does the duration of treatment have an impact on pain, functional status, and quality of life of patients? OBJECTIVE: Our aim was to investigate the role of application time on the efficacy of spa therapy in individuals undergoing the same treatment protocol. METHODS: This was a randomized, controlled, single-blind clinical trial design. Sixty individuals were divided into two groups: 3 weeks of therapy (group 1) and 2 weeks of therapy (group 2). Pretreatment, posttreatment, and 30-day posttreatment findings were recorded using a visual analog scale (VAS), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the Nottingham Health Profile (NHP). RESULTS: The measurements revealed significant improvement after treatment compared to those observed prior to the treatment in both groups, except for the social isolation subgroup. When the groups were compared to each other, significant improvement was observed in pain (VAS, WOMAC, NHP) in favor of group 1 at all measurement times. CONCLUSION: Our results suggest that spa therapy has positive effects on pain, physical activity, and quality of life in patients, and 15 sessions of therapy had even better effects on pain. We believe that the duration of therapy should be determined considering the efficacy of the therapy on pain as well as the functional situation and quality of life of patients regarding therapy costs.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".