Meta-analysis of the curative efficacy of Duhuo Jisheng Decoction combined with western medicine on knee osteoarthritis
Bibliographic record
Abstract
Objective: To evaluate the efficacy and safety of Duhuo Jisheng Decoction combined with \nwestern medicine in the treatment of knee osteoarthritis. Methods: The databases of CNKI, \nVIP, CBM, WANFANG DATA and PubMed were electronically to collect randomized \ncontrolled trials(RCTs) of the treatment of knee osteoarthritis with Duhuo Jisheng Decoction \ncombined with western medicine. After literature was strictly screened and the study quality \nwas evaluated according to relevant procedures, the Meta analysis was performed by \nRevMan5.3 software. Resluts: A total of 15 RCTs were included.The result of meta-analysis \nshowed that:compared with the western medicine alone, Duhuo Jisheng Tang combined with \nwestern medicine could significantly improve the efficacy(RR=1.25, 95%CI(1.18, 1.32)， \np<0.00001)，the posttreatment pain visual analogue scale scores(MD=-0.85, 95%CI(- \n1.00, -0.71)，p<0.00001)、the western ontario and mcmaster universities arthritis index \nscores(MD=-7.76, 95%CI(-10.37, -5.15), p<0.00001) and matrix metalloproteinase3(MD=- \n11.72, 95%CI(-17.39, -6.05), p<0.0001) were lower in treatment group compared to control \ngroup. There was no statistical difference in the incidence of adverse reactions between the \n2groups. Conclusions: The current evidence shows that Duhuo Jisheng Decoction combined \nwith western medicine can further improve the clinical efficacy for knee osteoarthritis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.056 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".