[Triple acupuncture method at cervical Jiaji (EX-B 2) for cervicogenic headache: a randomized controlled trial].
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy difference between triple acupuncture method at cervical Jiaji (EX-B 2) and conventional acupuncture for cervicogenic headache. METHODS: A total of 66 patients were randomly divided into a triple acupuncture group and a conventional acupuncture group, 33 cases in each one. The acupoints selected in the two groups were identical, including C1 to C7 of cervical Jiaji (EX-B 2) as well as Baihui (GV 20), Qiangjian (GV 18), Naokong (GB 19), Fengchi (GB 20), Fengfu (GV 16), Tianzhu (BL 10), etc. The conven- tional acupuncture was performed at acupoints on the head in the two groups; triple acupuncture was performed at cervical Jiaji (EX-B 2) in the triple acupuncture group while perpendicular acupuncture was performed with a depth of 25 to 40 mm in the conventional acupuncture group. The treatment was given once a day. Five treatments were considered as one session and totally two sessions were required. The changes of simplified McGill scale before and after acupuncture were observed in the two groups, and the efficacy of the two groups was compared. RESULTS: After acupuncture, the simplified McGill scale was both reduced in the two groups (both P < 0.01), and there was no significant difference between the two groups after treatment (all P > 0.05). The cured and markedly effective rate was 75.8% (25/33) and the total effective rate was 93.9% (31/33) in the triple acupuncture group, which were superior to 57.6% (19/33, P < 0.01) and 84.8% (28/33, P < 0.05) in the conventional acupuncture group respectively. CONCLUSION: The efficacy of triple acupuncture method at cervical Jiaji (EX-B 2) is superior to that of conventional acupuncture for cervicogenic headache.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".