Enhancing Duloxetine With Mirogabalin for Treating Taxane-Induced Peripheral Neuropathy in Advanced Lung Cancer
Bibliographic record
Abstract
Introduction Taxane-based cytotoxic anticancer drugs are a cornerstone of advanced lung cancer chemotherapy; however, they often result in chemotherapy-induced peripheral neuropathy (CIPN). Along with prolonged recovery, CIPN may cause irreversible damage. Consequently, dose reduction or discontinuation is justified, potentially impacting therapeutic efficacy. With no established treatment for CIPN, low-dose duloxetine is generally used as a supportive drug. However, studies have shown the potential effect of mirogabalin on CIPN. Therefore, at our hospital, patients with advanced lung cancer experiencing CIPN during taxane-based first-line therapy received low-dose duloxetine, and were subsequently treated with mirogabalin. Methods In this study, we conducted a retrospective observational cohort study of the impact of mirogabalin administration on 14 advanced lung cancer patients when duloxetine alone was deemed insufficient. The median age was 71 years (52-89 years), with 9 male and 5 female patients. The Numerical Rating Scale (NRS) was utilized to evaluate outcomes, and Wilcoxon’s signed rank-sum test was used in statistical analysis. Results The median Numerical Rating Scale (NRS) score decreased from 5.5 (interquartile range [IQR]: 4.5-7.0) before to 4.0 (IQR: 3.0-5.0) after mirogabalin administration ( P = 0.041), indicating significant pain reduction. Conclusion The addition of mirogabalin to duloxetine shows promise in alleviating CIPN in advanced lung cancer patients treated with taxane anticancer agents. These findings warrant further investigation and consideration for their integration into clinical practice for managing CIPN.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".