Therapeutic Efficacy of Mineral Bath Combined with Direct Current Vinegar Ion Import on Knee Osteoarthritis
Bibliographic record
Abstract
Objective To observe the therapeutic efficacy of the mineral bath in Xingcheng spring combined with direct current vinegar ion import on knee osteoarthritis.Methods A total of 123 patients with osteoarthritis were divided into therapy group and control group by simple random grouping method.The control group accepted mineral bath in Xingcheng and medium frequency electrotherapy.The therapy group accepted mineral bath and direct current introduction of vinegar ion.The cases were assessed with the osteoarthritis index from the University of Western Ontario and McMaster University(WOMAC osteoarthritis index) before,after and 3 months after the therapy to evaluate the efficacy.Results Before therapy,there was no statistical difference in WOMAC indexes between the two groups(P0.05).After therapy,the WOMAC index of both groups were improved and there was no statistical difference between two groups(P0.05).Three months after therapy,the WOMAC indexes of both groups were decreased in comparison with that after therapy and there was statistical difference between two groups(P0.05).Conclusion The therapy of mineral bath in Xingcheng combined with direct current vinegar ion import is effective on knee osteoarthritis especially in the long run.It helps to ease the pain,improve and restore the joint function,and improve the life quality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".