Evaluating the efficacy and safety of duloxetine and gabapentin in managing diabetic neuropathy: A systematic review and meta-analysis
Bibliographic record
Abstract
Background and objective Painful diabetic neuropathy (PDN) is a common complication of diabetes, characterized by significant pain and functional impairment. Gabapentin and duloxetine are standard treatments. This study compared their efficacy in alleviating pain, improving clinical global impression of change (CGIC), reducing sleep interference, enhancing response rates, and assessing safety. Methods A systematic review and meta-analysis was conducted following PRISMA guidelines. A search of Embase, Medline, ScienceDirect, Scopus, Web of Science, and Cochrane databases through May 2024 identified randomized controlled trials comparing gabapentin and duloxetine for PDN. Risk of bias was assessed using the Cochrane RoB2 tool. Data on pain, CGIC, sleep interference, responder rates, and adverse events were analyzed using a random-effects model, with results presented as standardized mean differences and risk ratios with 95% confidence intervals. Results Six RCTs with 526 patients (44% female) were included. There was no significant difference between duloxetine and gabapentin in relieving pain (SMD = −0.16, 95% CI [−0.36, 0.03], p = .10, I 2 = 66%). No significant differences were observed in the overall effect of CGIC (MD = 0.01, 95% CI [−0.07, 0.09], p = .79, I 2 = 0%), or sleep interference (MD = −0.07, 95% CI [−0.36, 0.23], p = .67, I 2 = 39%); However, duloxetine showed superiority at week 1 for CGIC (MD = 0.56, 95% CI [0.18, 0.94], p = .003), and week 8 for sleep interference (MD = −0.40, 95% CI [−0.79, −0.01], p = .04, I 2 = 0%), while gabapentin was superior at week 1 in sleep interference (MD = 0.75, 95% CI [0.11, 1.39], p = .02). No significant differences were observed in responder rates or adverse events. Conclusion Gabapentin and duloxetine are effective for PDN, with distinct advantage at different time points. Personalized treatment is recommended, and future research should assess long-term efficacy in diverse populations.
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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 | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".