Chinese herbal medicine Xianling Gubao capsule for knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is the most prevalent degenerative joint disease among populations over 60 years old, and is the most common cause of musculoskeletal pain and disability worldwide. Xianling Gubao capsule (XLGBC), a Chinese patent medicine, is widely used for treatment of osteoporosis. Meanwhile, according to the theory of homotherapy for heteropathy, XLGBC is increasingly applied in the clinical practice of KOA. However, no systematic review has found that XLGBC is as effective in treatment of KOA as it is in treatment of osteoporosis. Therefore, we will conduct a systematic review of XLGBC in KOA treatments. METHODS: All randomized controlled trials assessing the validity of XLGBC therapy for KOA will be retrieved from the following seven databases, including the Cochrane Library, PubMed, EMBASE, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, Wan Fang Database, and Chinese Scientific Journal Database. The primary outcome measures are the visual analogue scale pain score, and a comprehensive evaluation including the Western Ontario and McMaster Universities Arthritis Index scores, Lysholm scores, and Bristol scores. And the secondary outcome measures include cure rate and adverse events. The procedure such as retrieval and selection of literature, data extraction, evaluation of risk of bias, and assessment of reporting bias will be executed by 2 reviewers independently. The data synthesis for meta-analysis will be conducted by Review Manager 5.4 software. RESULTS: A high-quality evidence of XLGBC for the treatment of KOA will be generated from the aspects of safety and efficacy. CONCLUSION: This systematic review will provide evidence to help us confirm the clinical efficacy of XLGBC in the treatment of KOA. OSF REGISTRATION NUMBER: Registration DOI 10.17605/OSF.IO/QD5SY.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".