Cervicogenic headache treated by acupuncture based on jin theory: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Numerous randomized trials involving acupuncture treatment for cervicogenic headache (CEH) have been conducted in recent years, but the evidence for its effectiveness is not clear. Hence, we designed a randomized trial to evaluate the efficacy and advantages of acupuncture for treating CEH. DESIGN: This is a parallel-design, two-arm, patient-assessor blinded, randomized, sham-controlled clinical trial. A total of 166 patients with CEH aged from 18 to 70 will be recruited and assigned randomly into a jin acupuncture group and a pseudo acupuncture group at a 1:1 ratio; they will receive 12 sessions of real acupuncture and sham acupuncture for 4 weeks, respectively, during the study. The primary outcomes are pain degree (PD) and pain rate (PR) calculated by the PainVision analyzer, as well as parameters detected by surface electromyography (SEMG). The secondary outcomes will be measured with the short-form McGill Pain Questionnaire (SF-MPQ), range of motion (ROM) of the neck, the Northwick Park Neck Pain Questionnaire (NPQ), the 36-item short-form Health Survey (SF-36), the Self-Rating Anxiety Scale (SAS), and the Self-Rating Depression Scale (SDS). Clinical assessments will be evaluated at baseline and in the fourth week as well as in the eighth and sixteenth weeks. Adverse events will be noted and recorded for the safety evaluation. DISCUSSION: This study will provide high-quality evidence of the value of acupuncture based on jin theory for treating CEH. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR1800015316 . Registered on 22 March 2018. Updated version AMCTR-IOR-18000157 . Registered on 1 April 2018.
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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 | 0.038 | 0.030 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.010 |
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".