Duloxetine for prevention and treatment of chemotherapy-induced peripheral neuropathy (CIPN): systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Duloxetine has previously been reported to be promising in the setting of chemotherapy-induced peripheral neuropathy (CIPN). The aim of this study was to conduct a comprehensive systematic review and meta-analysis, on the use of duloxetine in prevention and treatment of CIPN. METHODS: PubMed, Embase and Cochrane CENTRAL were searched from database inception up until April 2022. Articles were included in this review if they reported on duloxetine use in the setting of CIPN, in a multiarm comparative human trial. A random effects DerSimonian-Laird model was used to calculate summary risk ratios (RR) and corresponding 95% CIs, comparing duloxetine to placebo. This review was registered on. RESULTS: Seven randomised controlled trials that included 645 patients were identified. Five reported on duloxetine for treatment of CIPN, and two for prevention of CIPN. Two studies had some concern for bias. Duloxetine was statistically similar to placebo in its efficacy, both in the treatment (RR 0.92, 95% CI 0.84 to 1.01) and prevention (RR 1.02, 95% CI 0.87 to 1.19) of CIPN. Safety profile was similar, in the treatment (RR 1.31, 95% CI 0.90 to 1.89) and prevention (RR 1.52, 95% CI 0.98 to 2.38) setting. CONCLUSION: There is currently limited evidence supporting duloxetine's use for CIPN. There is a need for more comprehensive and higher-quality trials assessing duloxetine in the setting of CIPN, before further clinical practice recommendations. TRIAL REGISTRATION NUMBER: PROSPERO (CRD42022327487).
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".