Key elements that determine the efficacy of cupping therapy: A bibliometric analysis and review of clinical studies
Bibliographic record
Abstract
The aim of this review was to identify the possible mechanisms behind cupping therapy by employing an evidence-based approach, and to explore its possible regional and systemic effects in the human body. We searched six electronic databases and four online trial registries for articles published up to January 1, 2020. Clinical studies on the mechanisms of retained cupping, flashing cupping, moving cupping, and vacuum cupping were considered for this review. The methodological qualities of controlled studies were assessed using the National Institute for Clinical Excellence methodology checklist, the Newcastle-Ottawa Scale, and the Cochrane risk of bias tool. Characteristic statistical description and qualitative summary of results were used for data analysis. Thirty-eight studies (37 full texts and one abstract) were included in this study. Due to the clinical heterogeneity among the studies, we could not conduct a meta-analysis. The results showed that the key factors that contribute to the efficacy of cupping therapy are negative pressure and temperature. Cupping therapy mainly causes local and systemic changes in hemodynamics, immune regulation, metabolism, and pain relief. We identified negative pressure as the key element behind cupping therapy. Cupping therapy may cause redistribution of oxygen at the cupping site and in neighboring tissues, thereby inducing a therapeutic effect by increasing regional blood flow. It may also induce metabolic change, immunomodulation, and neuromodulation. However, additional rigorous clinical research needs to be conducted to further clarify the mechanism behind cupping therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.023 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.097 | 0.113 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".