Acupuncture for diabetic neuropathic pain
Bibliographic record
Abstract
BACKGROUND: Diabetic neuropathic pain (DNP) is a common complication of diabetes mellitus, it severely affects the quality of life of Diabetic patients. Acupuncture is proofed to have favorable effects in treating DNP, however, evidence needs to be gathered and interpreted. We will make a comprehensive review of clinical trials concerning acupuncture in treating DNP and do meta-analysis if possible. METHOD: The following databases will be searched from the inception to September 2020: PubMed, Embase, Web of Science, China National Knowledge Infrastructure, Wan-Fang Database, and Chinese Scientific Journal Database. RCTs that evaluated acupuncture for patients with DNP will be included. The primary outcome will be patient-reported pain intensity using validated scales or verbal reporting. The secondary outcomes including the Toronto clinical scoring system, Sensory Nerve Conduction Velocity, Motor Nerve Conduction Velocity, and quality of life. The study selection, data extraction, and study quality evaluation will be performed independently by 2 researchers. A meta-analysis will be performed using RevMan V5.3 statistical software if possible; otherwise, descriptive analysis or subgroup analysis will be conducted. The quality of evidence for outcomes will be assessed with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. RESULTS: This study will evaluate the effect and safety of acupuncture in treating DNP. CONCLUSIONS: The evidence we generated from the present study will provide more options for DNP management in clinical practice. SYSTEMATIC REVIEW REGISTRATION: INPLASY202090043.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".