International Research Project to Devise a Protocol to Test the Effectiveness of Acupuncture on Painful Shoulder
Bibliographic record
Abstract
OBJECTIVES: To describe in detail the questions and deliberations leading to the development of a methodologically rigorous protocol to test the effectiveness of acupuncture on painful shoulder. DESIGN: Randomized controlled trial using three groups, including one control group. SETTINGS/LOCATION: A hospital in the north of Italy treating at least 8-10 painful shoulders a day, with physician/acupuncturists, physiotherapists, and assessors available to participate in the study. SUBJECTS: Sixty patients with monolateral painful shoulder. A list of exclusion criteria is given. INTERVENTIONS: Acupuncture + mobilization; mobilization alone (control); ear acupuncture + mobilization. OUTCOME MEASURES: Severity of pain, scored on a visual analogue scale (VAS) and on the McGill Pain Questionnaire and restriction of range of motion (ROM) of the shoulder joint. Voluntary use of pain medication, Tenoxicam 20 mg, was added to the protocol. RESULTS: The research team developed a protocol and methodology that avoids the common flaws and difficulties of previous clinical trials on acupuncture. CONCLUSIONS: Physicians and acupuncture specialists will benefit from the advice and support of a project group consisting of experienced clinicians, researchers, and statisticians when designing and preparing clinical trials on acupuncture and other complementary and alternative therapies.
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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 | 0.331 | 0.231 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.051 | 0.016 |
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".