Acupuncture for comorbid mild-moderate depression and chronic musculoskeletal pain: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Depression and chronic musculoskeletal pain (CMSP) are the leading causes of years lived with disabling diseases worldwide. Moreover, they often commonly coexist, which makes diagnosis and treatment difficult. A safe and effective treatment is urgently needed. Previous studies have shown that acupuncture is a cost-effective treatment for simple depression or CMSP. However, there is limited evidence that acupuncture is effective for depression comorbid with CMSP. METHODS: This is a randomized, sham acupuncture-controlled trial with three arms: real acupuncture (RA), sham acupuncture (SA), and healthy control (HC). Forty-eight depression combined CMSP participants and 12 healthy people will be recruited from GDTCM hospital and randomized 2:2:1 to the RA, SA, and HC groups. The patients will receive RA or SA intervention for 8 weeks, and HC will not receive any intervention. Upon completion of the intervention, there will be a 4-week follow-up. The primary outcome measures will be the severity of depression and pain, which will be assessed by the Hamilton Depression Rating Scale (HAMD-17) and Brief Pain Inventory (BPI), respectively. The secondary outcome measures will be cognitive function and quality of life, which will be measured by the Montreal Cognitive Assessment (MoCA), P300, and World Health Organization Quality of Life (WHOQOL-BREF). In addition, the correlation between brain-derived neurotrophic factor (BDNF) and symptoms will also be determined. DISCUSSION: The aim of this study is to evaluate the clinical efficacy and underlying mechanism of acupuncture in depression comorbid with CMSP. This study could provide evidence for a convenient and cost-effective means of future prevention and treatment of combined depression and CMSP. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1800014754 . Preregistered on 2 February 2018. The study is currently recruiting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".