A Traditional Herbal Formula Xianlinggubao for Pain Control and Function Improvement in Patients with Knee and Hand Osteoarthritis: A Multicenter, Randomized, Open‐Label, Controlled Trial
Bibliographic record
Abstract
Evidence of efficacy of a traditional herbal formula Xianlinggubao (XLGB) for treatment of osteoarthritis (OA) is limited. The present study was designed to evaluate the efficacy of XLGB in the management of patients with knee and hand OA. This was a multicenter, stratified, open‐label, randomized controlled trial conducted at six centers in China. People aged 40 or above, diagnosed with OA of the knee or hand, were randomly assigned to the XLGB treatment group or watchful waiting control group. Main outcome measures were the changes in the numeric pain rating scales (NPRS) and the Western Ontario and McMaster Universities Arthritis Index (WOMAC) or the Australian/Canadian Osteoarthritis Hand Index (AUSCAN) scores, from baseline to 6 months. In total 534 patients (272 to XLGB and 262 to control group) received interventions. Participants in the XLGB group exhibited significant improvement in NPRS (P < 0.001) and WOMAC score (P < 0.001) or AUSCAN score (P < 0.001) compared to control group. Treatment with XLGB at current regime significantly reduced pain and improved function of the knee and hand in patients with OA over a 6‐month period, implying that XLGB could be suggested as an alternative treatment for patients with knee or hand 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".