Skin Temperature of Acupoints in Health and Primary Dysmenorrhea Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Xuesong Wang,1,* Guang Zuo,1,* Jun Liu,1,2 Juncha Zhang,1,2 Xuliang Shi,1,2 Xisheng Fan,1,2 Xuxin Li,1 Yuanbo Gao,1 Hao Chen,1 Cun-Zhi Liu,3 Yanfen She1,2 1School of Acupuncture-Moxibustion and Tuina, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, People’s Republic of China; 2Hebei International Joint Research Center for Dominant Diseases in Chinese Medicine and Acupuncture, Hebei University of Chinese Medicine, Shijiazhuang, Hebei, People’s Republic of China; 3School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jun Liu; Yanfen She, School of Acupuncture-Moxibustion and Tuina, Hebei University of Chinese Medicine, No. 3 Xingyuan Road, Luquan Districtt, Shijiazhuang City, Hebei Province, 050200, People’s Republic of China, Email 822053583@qq.com; sheyanfen@163.comObjective: Dysmenorrhea is a common clinical condition and some studies shown that the skin temperature of some acupoints changes in primary dysmenorrhea (PD) patients. This study aimed to evaluate the changes in skin temperature at specific acupoints in PD patients and healthy subjects.Methods: The literature for assessing skin temperature at acupoints in PD patients and healthy subjects was searched in eight databases. The literatures obtained from the search was independently screened by two authors, and the quality of the included articles was evaluated using the consensus checklist of the Thermographic Imaging in Sports and Exercise Medicine (TISEM) and the Newcastle–Ottawa Scale (NOS) scale. The skin temperature of the relevant acupoints or the difference between the left and right acupoints of the same name was used as the outcome during any period of menstruation. Finally, the meta-analysis was performed using RevMan 5.4.1 software to evaluate the changes in skin temperature in the related acupoints.Results: Seven eligible studies were included, which included 328 patients with PD and 279 healthy subjects. The results of the meta-analysis revealed a significant difference in skin temperature around the Sanyinjiao (SP6)(MD: 0.04, 95% CI: 0.00, 0.08), Xuehai (SP 10)(MD: − 0.07, 95% CI:-0.11, − 0.02) and Taixi (KI 3)(MD: 0.06, 95% CI:0.01, 0.11) acupoints between PD and healthy subjects. PD patients also showed a difference in skin temperature at the Taixi (KI 3)(MD: 0.14, 95% CI:0.04, 0.24), Shuiquan (KI 5)(MD: 0.11, 95% CI: 0.03,0.19), Taichong (LR 3)(MD: − 0.10, 95% CI: − 0.19,-0.01), Diji (SP 8)(MD: − 0.09, 95% CI: − 0.16, − 0.01), and Xuehai (SP 10)(MD: − 0.14, 95% CI: − 0.23, − 0.06) acupoint areas at different times of menstruation compared to that of healthy subjects, as revealed by the subgroup analysis.Conclusion: Primary dysmenorrhea patients showed some differences in the skin temperature of the special acupoints are as Sanyinjiao (SP6), Diji (SP 8), Xuehai (SP 10), Shuiquan (KI 5), Taichong (LR 3), and Taixi (KI 3) compared with healthy subjects.Registration Number: CRD42022381387.Keywords: acupuncture points, acupoints, acupoint sensitization, skin temperature, thermography, meta-analysis, systematic review
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".