Defining the Mechanism of Action of Herbal Therapies in Rheumatoid Arthritis: Is This the Road to Clinical Development and Acceptance?
Bibliographic record
Abstract
Herbal treatments are widely used to treat chronic inflammatory disorders such as rheumatoid arthritis (RA). This is particularly the case in China, where these treatments have been part of traditional medicine practice, in some cases for centuries. In the current age of targeted therapies for RA, where efficacy, safety, and mechanism of action are demonstrated using widely accepted scientific methodology, it remains challenging to determine how the spectrum of herbal and other traditional therapies should fit into modern therapeutic paradigms. Patients not uncommonly gravitate toward these treatments because of their “natural” origins, their perceived safety, and their relatively modest cost. Yet in most cases, evidence for clinical efficacy, safety, and mode of action is lacking, leading many clinicians to advise their patients against the use of such treatments until better evidence is available. A comprehensive analysis of the available clinical evidence for a spectrum of herbal therapies used in the treatment of RA has recently been updated in the Cochrane Library1. This database is a valuable resource in helping clinicians begin to navigate through the confusing and often contradictory information that is widely available regarding use of these therapies and their influence on disease. In most cases, even less is known about how any individual agent potentially affects the complex molecular pathways underlying the clinical phenomenology seen in RA. In this issue of The Journal , Hsu, et al begin this challenging task in relation to Plectranthus amboinicus , a herbal therapy widely used in Taiwan to treat RA and other inflammatory disorders2. One of the best-studied herbal treatments in RA to date has been thunder god vine ( Tripterygium wilfordii hook F, TwHF). Several randomized clinical trials have demonstrated efficacy3,4,5, with the most recent showing benefits that were at least … Address correspondence to Dr. El-Gabalawy. E-mail: elgabalh{at}cc.umanitoba.ca
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".