Deqi effect for treating knee osteoarthritis using manual acupuncture: A network meta-analysis
Bibliographic record
Abstract
To explore whether the deqi effect affects the effectiveness of manual acupuncture for treating knee osteoarthritis by a network meta-analysis (NMA). Eight databases were searched for relevant published studies since inception through January 2021. Randomized control trials that compared manual acupuncture with deqi, manual acupuncture without deqi, and conventional treatment of western medicine were included. Languages were limited to English and Chinese. Two authors independently selected eligible studies, assessed methodological quality and the effects of deqi, and extracted outcome data. The mean difference (MD) and standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated by Stata 15.0 to conduct an NMA based on a frequentist framework. Fourteen trials with a total of 1868 subjects were included. The direct and indirect comparisons were authentic, according to inconsistency tests. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pooled results indicated that there was no significant effect of deqi (SMD = −0.23, 95% CI [−0.81, 0.35]). The pooled results of visual analogue scale (VAS) were similar to those of WOMAC (MD = −0.53, 95% CI [−1.03, 0.02]). As a summary of the currently available research, the NMA results indicated that deqi effect might not be the crucial factor for the effectiveness of manual acupuncture.
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
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 | 0.033 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.043 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".