Ringer′s solution and Synvisc in treatment of knee osteoarthritis: a contrast study
Bibliographic record
Abstract
Objective To explore the effects of Ringer′s solution and Synvisc in treatment of knee osteoarthritis(OA).Methods A total 58 outpatients with knee osteoarthritis were randomly divided into three groups.Group A accepted simple intra-articular irrigation of Ringer′s solution,group B accepted simple Synvisc injection and group C accepted both intra-articular irrigation of Ringer′s solution and Synvisc injection.Westem Ontario and McMaster University Osteoarthritis Index(WOMAC) pain score were used to assess the effects before treatment and 1,8,12,24 and 52 weeks after treatment.All treatments were finished by the same doctor group in the Qingdao Municipal Hospital by a patient-blinded method.All analyses were performed by the Stata software.Results All three groups had the WOMAC pain scores significantly improved at 8,12 and 24 weeks(P0.05),and groups B and C still had significant effects at 52 weeks(P0.05).Groups B and C were superior to group A in clinical efficacy(P0.05),and group C was superior to group B in pain relief at 1-week term(P0.05).Conclusions Either Ringer′s solution or Synvisc injection can significantly improve the WOMAC pain scores for knee osteoarthritis,and Synvisc injection is superior to Ringer′s solution in pain-relief,duration and operative briefness,but there are no folded effects when they are used in combination.
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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 | 0.001 |
| 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 | 0.001 |
| 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".