Symptom Combinations Assessed in Traditional Chinese Medicine and Its Predictive Role in ACR20 Efficacy Response in Rheumatoid Arthritis
Bibliographic record
Abstract
The predictive roles of symptom combination traditionally evaluated in traditional Chinese medicine (TCM) in the treatment of rheumatoid arthritis (RA) were explored. Three hundred and ninety six patients were randomly divided into 197 subjects receiving Western medicine therapy (WM) and 199 subjects receiving TCM therapy (TCM). A complete physical examination and 18 clinical manifestations typically assessed in TCM were recorded before the randomization. The ACR responses were used for efficacy evaluation. ACR20 and 50 responses with WM treatment were higher than in the TCM group. The 18 symptoms in RA could be clustered into 4 symptom combinations with factor analysis, which represent joint symptoms, cold pattern, deficiency pattern and hot pattern in TCM respectively. TCM would be more effective in patients with weak-symptom combination 3 (deficiency pattern in TCM), and WM would be more effective in patients with symptom combination 2 (cold pattern in TCM). Symptom combinations judged with TCM may have influence on the efficacy of therapy in the treatment of RA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".