Electro-acupuncture for health-related quality of life and symptoms in patients with gastric cancer undergoing adjuvant chemotherapy (EAGER): a protocol for a multicenter randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Patients with local gastric cancer experience a decline of Health-related quality of life (HRQOL) during adjuvant chemotherapy following gastrectomy. Our previous pilot study has indicated the potential of acupuncture to improve HRQOL and control cancer-related symptoms burden. This full-scale trial will focus on confirming the effect of acupuncture for patients with gastric cancer. METHODS: A multicenter, open-label, three-arm randomized controlled trial with 249 patients will be conducted in China. Patients will be randomly assigned, in a ratio of 1:1:1, to receive high-dose acupuncture (HA, 7 times each chemo-cycle for 3 cycles), low-dose acupuncture (LA, 3 times each chemo-cycle for 3 cycles), or no acupuncture. The acupoints prescription consisted of bilateral ST36, PC6, SP4, DU20, EX-HN3, and selected Back-shu points. Patients-reported Functional Assessment of Cancer Therapy-Gastric (FACT-Ga) and modified Edmonton Symptom Assessment Scale (mESAS) during the therapy will be recorded. Area under curve (AUC, 21 days/cycle × 3 cycles) and average trajectory of FACT-Ga and mESAS will be calculated. The primary outcome will be the differences in AUC of the FACT-Ga Trial Outcome Index (TOI) between HA and LA versus control groups. Secondary outcomes include AUCs and average trajectory of other FACT-Ga subscales and mESAS scores. DISCUSSION: This study aims to assess the effect of acupuncture and to compare the difference between LA and HA groups on HRQOL and symptom burden controlling in gastric cancer patients by an adequately powered trial. TRIAL REGISTRATION: This study was approved by the Ethics Committee of the Guangdong Provincial Hospital of Traditional Chinese Medicine (approval number: BF2018-118) with registration at ClinicalTrials.gov (identifier: NCT04360577).
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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 | 0.036 | 0.022 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".