Evaluation of the efficacy of Tongdu Shujin Decoction combined with Adalimumab in the treatment of ankylosing spondylitis: A study protocol for a randomized controlled trial
Bibliographic record
Abstract
Abstract Introduction Ankylosing spondylitis (AS) is a chronic inflammatory disease that typically affects the axial skeleton and entheses, and can lead to severe physical and psychological damage to patients. However, the current therapies have limitations.Methods and analysis Eighty-patients with AS will be recruited from the Rheumatology Department of the First Affiliated Hospital of the Henan University of Chinese Medicine. The participants will be randomly divided into the treatment and control groups at a 1:1 ratio. Subsequently, all subjects in the treatment group will receive 40 mg adalimumab administered subcutaneously every two weeks for 12 weeks, while patients in the control group will receive an extra dose of Tongdu Shujin Decoction (TDSJ) each day. The primary outcome will be determined by the change in the Ankylosing Spondylitis Disease Activity Score (ASDAS) from baseline to 12 weeks. The secondary outcomes include changes of serum inflammatory cytokines, interleukin-6 (IL-6), interleukin-17 (IL-17), Tumor necrosis factor alpha (TNF-α), changes of anxious depression-related scales, Hospital Anxiety and Depression Scale (HADS) and Patient Health Questionnaire-9 (PHQ-9) as well as changes of McGill Pain Questionnaire (MPQ) and Fatigue Severity Scale (FSS) in patients from baseline to 12 weeks.Ethics and dissemination The ethics committee of the First Affiliated Hospital of Henan University of Chinese Medicine has approved the study on April 27, 2023 (2023HL-116-02). The Chinese national authorities issued approval on 15 May 2023. The outcome of the study will be disseminated via peer-reviewed publications and at congresses.Trial registration number Chinese Clinical Trial Registry. ChiCTR2300071418. Registered on 15th May 2023. https://www.chictr.org.cn/showproj.html?proj=196506
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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 | 0.033 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".