The Effect of Acupuncture on Chemotherapy-Associated Gastrointestinal Symptoms in Gastric Cancer
Bibliographic record
Abstract
Background: Gastrointestinal (gi) symptoms are the most notable side effects of chemotherapeutic drugs; such symptoms are currently treated with drugs. In the present study, we investigated the effect of acupuncture on gi symptoms induced by chemotherapy in patients with advanced gastric cancer. Methods: A cohort of 56 patients was randomly divided into an experimental group and a control group. All patients received combination chemotherapy with oxaliplatin–paclitaxel. Patients in the experimental group received 30 minutes of acupuncture therapy daily for 2 weeks. The frequency and duration of nausea, vomiting, abdominal pain, and diarrhea, the average days and costs of hospitalization, and quality-of-life scores were compared between the groups. Results: Nausea was sustained for 32 ± 5 minutes and 11 ± 3 minutes daily in the control and experimental groups respectively (p < 0.05). On average, vomiting occurred 2 ± 1 times daily in the experimental group and 4 ± 1 times daily in the control group (p < 0.05). Abdominal pain persisted for 7 ± 2 minutes and 16 ± 5 minutes daily in the experimental and control groups respectively (p < 0.05). On average, diarrhea occurred 1 ± 1 times daily in the experimental group and 3 ± 1 times daily in the control group (p < 0.05). The average quality-of-life score was higher in the experimental group than in the control group (p < 0.05). No adverse events were observed for the patients receiving acupuncture. Conclusions: Acupuncture, a safe technique, could significantly reduce gi symptoms induced by chemotherapy and enhance quality of life in patients with advanced gastric cancer.
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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".