Acupuncture to Improve Symptoms for Stable Angina (AIMS-A): Study Protocol for a Randomized Controlled Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Acupuncture has demonstrated physiologic analgesic effects in Chinese patients with stable angina. One proposed mechanism of action for analgesic effects is the downregulation of M1 macrophages, Interleukin-1β, Interleukin-6, Interleukin-18, and tumor necrosis factor alpha (TNF-α). OBJECTIVE The purpose of this study is to test a 10-session, 5-week acupuncture treatment protocol as a complementary therapy for symptoms of stable angina for American patients, who vary from Chinese patients in healthcare systems and other salient variables. METHODS We are conducting a randomized controlled trial (RCT) of 69 adults (35 assigned to initial acupuncture and 34 to an attention control condition) with a medically confirmed diagnosis of stable angina, whose pain and associated symptoms have not been controlled to their satisfaction with guideline-directed medical management. Participants in the experimental group will receive a standardized Traditional Chinese Medicine (TCM) point prescription. The attention control group will view non-pain-related health education videos over 5 weeks equal to the 10 hours of treatment for the acupuncture group. Participants will complete the McGill Pain Questionnaire and the Seattle Angina Questionnaire-7, as well as have inflammatory cytokines measured at baseline and study completion. Primary outcomes are anginal pain and quality of life. RESULTS Results will be available upon completion of the randomized controlled trial and the analysis of the data. CONCLUSIONS We will generate data on feasibility, acceptability, effect sizes, and protocol revisions for a future fully powered RCT of the protocol. Findings will help determine if patients with persistent ischemic symptoms experience a pro-inflammatory state and/or hyperalgesia caused by multiple neural and immune processes not always relieved with medication. CLINICALTRIAL ClinicalTrials.gov Identifier: NCT02914834
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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 | 0.027 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.101 | 0.014 |
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".