Comparative Study of the Effects of Duloxetine and Venlafaxine on Acute Symptomatic Taxane-induced Neuropathy in Breast Cancer Patients: a randomized clinical trial
Bibliographic record
Abstract
Introduction: Chemotherapeutic agents have the potential to induce neurotoxicity, resulting in a range of symptoms, including mild paresthesia, neuropathic pain, pronounced ataxia, and significant impairment. Taxane-induced neuropathy (TIN) is a prevalent adverse effect and a significant constraint of Taxane-based chemotherapy protocols in treating breast cancer. In this current study, we aim to compare the effects of Venlafaxine and Duloxetine in taxane-induced Neuropathy as well as the quality of life, Depression, and Anxiety in Breast cancer Patients. Methods: The present study investigated breast cancer patients who experienced acute neuropathic pain after receiving paclitaxel treatment, a chemotherapeutic agent. The participants were allocated randomly into two groups, one receiving Venlafaxine and the other receiving Duloxetine. The participants underwent assessments for anxiety, depression, pain, neuropathy, quality of life, and neuropathic pain through the administration of questionnaires at the commencement of the study and after ten weeks following the intervention. Results: Both groups exhibited decreased neuropathic pain, with the venlafaxine group significantly reducing McGill's pain score. Although, the result is not suggestive of a difference between venlafaxine and duloxetine impact on any variables scores. Conclusion: Duloxetine and Venlafaxine effectively treat neuropathic symptoms such as paraesthesia, tingling, and itching. Venlafaxine is also beneficial for relieving pain associated with neuropathy.This trial was retrospectively registered on 1.1.2023 at irct.ir (trial registration ID: IRCT20220115053723N1). URL: https://www.irct.ir/trial/62540/pdf.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".