Effect of Transcranial Direct Current Brain Stimulation of the Motor Cortex on Chemotherapy-Induced Nausea and Vomiting in Female Patients with Breast Cancer
Bibliographic record
Abstract
OBJECTIVE: Chemotherapy-induced nausea and vomiting (CINV) is an adverse outcome associated with chemotherapy and is sometimes difficult to manage. This study aimed to examine the impact of a single session of transcranial direct current brain stimulation (tDCS; 2 mA) over the motor cortex for 20 minutes before chemotherapy in patients receiving a highly emetogenic chemotherapy. STUDY DESIGN: Prospective randomized double-blind sham-controlled study. SETTING: Academic medical center. METHOD: Sixty patients with breast cancer who were scheduled for chemotherapy treatment were selected and allocated randomly into two equal groups: a stimulation group and a sham group. tDCS was implemented over the primary motor area (M1) (2 mA) for 20 minutes. Patients' nausea was measured by a cumulative index of nausea, a visual analog scale for nausea (VAS-N), episodes of vomiting, and the Edmonton Symptoms Assessment Scale (ESAS) to assess symptoms like pain, malaise, and sense of well-being. Evaluation was done before stimulation and every 24 hours for 72 hours after the end of infusion of chemotherapy. RESULTS: The tDCS group showed a reduction in the cumulative index of nausea (P < 0.001, F = 50), the VAS-N (P < 0.001, F = 52), the ESAS malaise score (P < 0.001, F = 37.6), and the sense of well-being score (P < 0.001, F = 25) vs the sham group. Six patients (20%) in the tDCS group required rescue antiemtic therapy vs 14 patients (46.7%) in the sham group (P < 0.028). CONCLUSION: A single session of M1 tDCS is suggested as an effective adjuvant therapy to control CINV in female patients suffering from breast cancer and receiving highly emetogenic chemotherapy. Corroboratory studies are needed.
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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".