The effect of thermal mineral waters on pain relief, physical function and quality of life in patients with osteoarthritis
Bibliographic record
Abstract
BACKGROUND: To evaluate the effectiveness and safety of thermal mineral waters therapy for pain relief, and functional improvement, and quality of life (QoL) in patients with osteoarthritis (OA). METHODS: Cochrane Library, Web of science, EMBASE, ClinicalTrials.gov and PubMed were systematically searched for randomized controlled trials. Study inclusion criteria included assessment of the visual analog scale and Western Ontario and McMaster Universities scores and the lequesne index to evaluate the effects of thermal mineral waters on pain relief and functional improvement. Also, studies that used the European quality of life 5-dimension scale and health assessment questionnaire to assess the impact of thermal mineral waters therapy on improving QoL were included. RESULTS: Sixteen studies were included. A meta-analysis showed that thermal mineral waters therapy could significantly reduce pain as measured visual analog scale and Western Ontario and McMaster Universities assessments (P < .001). Thermal mineral waters significantly reduced the lequesne index (P < .001) and improved joint function. Finally, compared with a control group, European quality of life 5-dimension scale and health assessment questionnaire improved significantly in patients with OA receiving thermal mineral waters therapy (P < .05). There is no evidence that thermal mineral waters is unsafe for treating OA. CONCLUSION: Thermal mineral waters therapy is a safe way to relieve pain, improve physical functions, and QoL in patients with OA.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".