543-P: A Randomised Control Trial to Evaluate the Efficacy of Chinese Traditional Medicine for the Treatment of Diabetic Peripheral Neuropathy
Bibliographic record
Abstract
Background: Diabetic peripheral neuropathy (DPN) is a common complication in patients (pts) with diabetes. However, multinational data do not support any convincing efficacy of the current therapies for treating DPN. This necessitated the evaluation of safety and efficacy of alternative medicine to tackle DPN. Methods: A double-blinded RCT was conducted in 8 Chinese medicine hospitals in China, evaluating the safety and efficacy of 190 g Tangbi Waixi decoction (TW, intervention (Int)) compared with 19 g TW (control (Cont)). Change in Toronto Clinical Scoring System - Total Score (TCSS-TS) at 12 and 24 weeks follow-up was the primary outcome, with treatment ceasing at 12 weeks. Secondary outcomes included change in bilateral motor velocity (BMV) and sensory nerve conduction velocity (SNCV) of the common peroneal nerve (CPN). All pts received standard care for glycaemic management. The pts in the Int (n=317) and Cont (n=315) groups were analysed on ITT principle. All adverse events (AEs) were recorded. Results: The pts were mean 60 years old with median 10 yrs and 2 yrs of diabetes and DPN duration respectively at randomisation. With median TCSS-TS of 10 at randomisation in both groups, the median (95% CI) reduction in Int and Cont: at 12 weeks 2 (1.7, 2.3) and 3 (2.7, 3.3) respectively (median between group difference (MBGD): 1.0, p=0.011); at 24 weeks 3 (2.6, 3.4) in both groups. At 12 weeks, median (95% CI) CPN velocity increased in both Int and Cont - BMV Int vs. Cont: 2.7 (1.9, 4.9) m/s vs. 4.9 (2.7, 5.5) m/s, MBGD - 2.5 m/s (p=0.044); SNCV Int vs. Cont: 1.4 (0, 2) m/s vs. 2.7 (1.9, 4.9) m/s, MBGD - 0.7 m/s (p=0.26). Similar increase in CPN velocity was observed at 24 weeks in both groups. Overall 12% pts experienced at least one AE, with no difference between groups. Conclusion: Treatment of DPN with TW decoction significantly benefitted the pts and is safe, while lower dose of TW appears to be more effective than the higher does. Disclosure F. Guanjie: None. Y. Lin: None. S.K. Paul: Advisory Panel; Self; Sanofi. Research Support; Self; AstraZeneca, Genentech, Inc. H. Huang: None. T. Xianyu: None. Z. Yuehong: None. F. Zhaohui: None. Q. Xiaotang: None. D. Fang: None. S. Wang: None. M. Wang: None. H. Wei: None. Z. Ling: None. L. Zhenjie: None. Y. Fu: None. M. Wu: None. L. Anxiang: None. H. Jinzhu: None. Q. Li: None. Funding National Science and Technology Support Program (2015BAI04B09)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".