Robust Evidence in Integrative Medicine: Innovations, Challenges, and Future Directions
Bibliographic record
Abstract
Integrative Medicine (IM), which includes therapies such as acupuncture, herbal medicine, yoga, and meditation, is gaining attention for managing chronic pain conditions. However, concerns about the quality of evidence supporting the use of these interventions persist. The 5th Annual Jaseng Academic conference 2024, in Seoul, South Korea, themed "Robust Evidence in Integrative Medicine: Innovations, Challenges, and Future Directions," addressed these concerns by focusing on advancements in study design, evidence synthesis, and open science practices. This conference proceeding summarizes key insights from the conference, emphasizing the role of pragmatic randomized controlled trials (pRCTs) in evaluating real-world effectiveness, and addressing the complexities involved in IM research such as sham controls. The integration of IM therapies into comprehensive pain management strategies (particularly in Korea), supported by government-backed research and policy initiatives was also discussed. Advancements in methodologies were addressed, such as bibliometric analysis, evidence mapping, and the development of clinical practice guidelines (CPGs) for integrative therapies. These methodologies offer valuable insights but face challenges due to the heterogeneity of IM interventions, and potential synergistic or antagonistic effects when combined with conventional medicine. Finally, the potential of open science to enhance transparency, reporting, and reproducibility in IM was explored, emphasizing the increased role of adherence to reporting guidelines (CONSORT and PRISMA). The future of IM research is built upon the continued efforts of refined study designs, rigorous evidence synthesis, and the integration of open science principles, for a robust and more credible evidence base.
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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 | 0.473 | 0.413 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.028 | 0.046 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.020 | 0.033 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".