Whole Complementary and Alternative Medical Systems and Complexity: Creating Collaborative Relationships
Bibliographic record
Abstract
In recent years, investigators have discovered significant limitations in applying biomedical cause-effect assumptions and using conventional efficacy study designs to assess the clinical outcomes of whole systems of complementary and alternative medicine (WS-CAM). A group of WS-CAM researchers has been working collaboratively since 2001 to address the limitations of studies evaluating WS-CAM and discern ways to conduct research that would capture the complexity of such systems and the synergistic effects between the various elements of the system and would take into account treatment individualization and/or the patient-centered nature of treatment systems. In 2009, 14 complexity scientists from systems biology, psychology and the social sciences were invited to attend a workshop with these CAM scientists to (a) identify and discuss analytical techniques that can be used to study phenomena from a complex/nonlinear dynamical sciences perspective, (b) establish working relationships with these researchers, and (c) develop working research projects/ protocols to collaboratively study patient-centered responses to CAM treatments. This paper provides an overview of the workshop goals and outcomes, introducing this special issue of Forschende Komplementärmedizin.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".