The clinical efficacy of epalrestat combined with α-lipoic acid in diabetic peripheral neuropathy
Bibliographic record
Abstract
BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common long-term complication of diabetes mellitus, affecting patients in the world. Epalrestat combined with α-lipoic acid (ALA) is the most frequent combine therapy used in the DPN researches. We aim to assess the effectiveness and safety of epalrestat combined with ALA in patients with DPN, compare with epalrestat alone. METHODS: We will search Cochrane Library, PubMed, Wanfang Data, China National Knowledge Infrastructure, VIP Chinese Science and Technology Journals Database, and Chinese Biomedical Database from inception until October 31th, 2017. Inclusion the randomized controlled trials and clinical control trials of combine therapy which evaluate clinical efficacy and side effect in people with DPN. Data extraction and risk of bias assessments will be independently conducted by 2 reviewers. The primary outcome measures will be total effective rate, motor nerve conduction velocity (MNCV), sensory nerve conduction velocity (SNCV), Toronto clinical scoring system (TCSS), and total symptom score (TSS). All statistical analyses will be performed using RevMan V.5.3 software. RESULTS: This review will evaluate the total effective rate, nerve conduction velocity, TCSS, TSS, and safety of ALA combined with epalrestat for patients with DPN, compare with epalrestat alone. CONCLUSION: Our study will provide evidence to assess whether epalrestat combined with ALA is an optional treatment for patients with DPN.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".