Chinese Medicinal Formula (MHGWT) for Relieving Diabetic Neuropathic Pain: A Randomized, Double-Blind, Placebo-Controlled Trial
Bibliographic record
Abstract
Objective. To investigate the effects of modified Hungqi Guizhi Wuwu Tang (MHGWT), a formula that comprises Chinese medicinal herbs, in relieving neuropathic pain in diabetics. Method. Between March 2008 and April 2009, 112 participants were randomly assigned to either the MHGWT group, whose members received MHGWT (n = 56), or the control group, whose members received a placebo (n = 56). Diabetic neuropathic pain (DNP) was rated using the 15-item Short-Form Brief Pain Inventory (SF-BPI), the 17-item Short-Form McGill Pain Questionnaire (SF-MPQ), the 13-item Modified Michigan Neuropathy Screening Instrument (MMNSI), and the 36-item "SF-36." Nerve conduction studies (NCSs) were performed before and after treatment. Results. After 12 weeks of treatment, the SF-MPQ and SF-BPI scores of the MHGWT group were significantly (P < 0.05) reduced and a significant difference between the groups was observed (P < 0.05). The levels of NCS in the MHGWT group were nonsignificantly (P > 0.05) reduced, and no significant difference in NCS level was observed between the groups (P > 0.05). Conclusions. MHGWT shows promise in relieving DNP and deserves further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".