Comparison Between Superficial and Deep Acupuncture in the Treatment of the Shoulder’s Myofascial Pain: A Randomized and Controlled Study
Bibliographic record
Abstract
The principle aim of the present work is the evaluation of the importance of the variable "depth" of needle insertion in the determination of acupuncture's therapeutic results. A randomized clinical trial carried out on 44 patients belonging to two groups of 22 each, suffering from shoulder myofascial pain: group A: superficial acupuncture; group B: deep acupuncture. All of them subjected to the same model of treatment with 13 needles and the treatment of the most painful 4 trigger points (TP) found in the shoulder's area. In the group A patients, the needles were introduced in the skin at a depth of 2 mm, on the TP. In the group B patients, the needles were placed deeply both in the muscular acupuncture points and in the TP. The treatment was planned for a cycle of 8 sessions, the first 4 to be performed twice a week, the last 4 weekly. The intensity of pain was evaluated with the McGill Pain Questionnaire before beginning the therapy, at the end of the therapy, and at the follow-up after 1 and 3 months. Both techniques had efficacy in controlling pain. A statistically significant difference rose between the two needling techniques at the end of the treatment and at the follow up after one and three months. Deep acupuncture shows to be better at all times and this underlines the importance of the muscular afferences in acupunctural stimulation in the control of pain.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".