[Effect of acupuncture on patients with cancer-related fatigue and serum levels of CRP, IL-6, TNF-α and sTNF-R1].
Bibliographic record
Abstract
OBJECTIVE: To observe the therapeutic effect of acupuncture on cancer-related fatigue (CRF) and to explore its possible mechanism. METHODS: A total of 80 patients with CRF were randomized into an observation group and a control group, and finally 67 patients completed the trial (36 patients in the observation group, 31 patients in the control group). Patients in the control group were treated with conventional chemoradiotherapy and symptomatic treatment, while no particular anti-fatigue intervention was adopted. On the basis of treatment in the control group, acupuncture was applied at Baihui (GV 20), Guanyuan (CV 4), Qihai (CV 6), Fengchi (GB 20), Zusanli (ST 36), Sanyinjiao (SP 6) in the observation group, once a day, 5 times as one course, with 2 days interval between each course, totally 4 courses were required. Before and after treatment, scores of functional assessment of cancer therapy-fatigue (FACT-F) in Chinese and McGill quality of life questionnaire (MQOL), serum levels of C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-α(TNF-α) and soluble TNF receptor-1 (sTNF-R1) were observed in the two groups. RESULTS: <0.05). CONCLUSION: ①Acupuncture can improve the related symptoms of depression, weakness and headache in patients with CRF, strengthen their cognition of the support from society and family, and boost the confidence in curing the disease. ②Acupuncture can effectively down-regulate serum levels of the relative inflammatory factors, which may be its possible mechanism on treating CRF.
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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) | 0.002 | 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".