Impact of Bad Ragaz ring in hot spring water on knee osteoarthritis: A prospective observational study
Bibliographic record
Abstract
To evaluate the impact of the Bad Ragaz ring method (BRRM) in hot spring water for knee osteoarthritis (KOA), this prospective study enrolled KOA patients treated at the hospital between March 2020 and December 2020. The primary outcome was the Western Ontario and McMaster Universities (WOMAC) osteoarthritis index score. A total of 60 patients were included, with 30 participants in the BRRM group and 30 patients in the non-BRRM group, respectively. The mean age was 56.4 ± 10.2 years (13 females), and the duration of disease was 5.0 ± 2.2 years in the BRRM group. The mean age was 56.0 ± 11.3 years (14 females), and the disease duration was 4.7 ± 2.1 years in the non-BRRM group. There were no differences between the 2 groups in the pain, stiffness, and function scores of the WOMAC (all P > .05) before treatment. The pre post difference in total WOMAC scores (56.57 ± 12.45 vs 36.81 ± 13.51, Cohen d = 1.52, P < .01) between the 2 groups was statistically significant. Compared with the non-BRRM group, the BRRM group showed lower scores for pain (6.5 ± 1.5 vs 8.1 ± 2.9, Cohen d = -0.69, P = .01), stiffness (2.7 ± 1.0 vs 5.0 ± 1.2, Cohen d = -1.93, P < .01), and function (14.8 ± 6.6 vs 26.7 ± 7.5, Cohen d = -1.68, P < .01) after treatment. In conclusion, the BRRM might improve the pain and function of patients with KOA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".