Bibliographic record
Abstract
This article summarizes a network and complex systems science model for research on whole systems of complementary and alternative medicine (CAM) such as homeopathy and traditional Chinese medicine. The holistic concepts of networks and nonlinear dynamical complex systems are well matched to the global and interactive perspectives of whole systems of CAM, whereas the reductionistic science model is well matched to the isolated local organ, cell, and molecular mechanistic perspectives of pharmaceutically based biomedicine. Whole systems of CAM are not drugs with specific actions. The diagnostic and therapeutic approaches of whole systems of CAM produce effects that involve global and patterned shifts across multiple subsystems of the person as a whole. For homeopathy, several characteristics of complex systems, including the probabilistic nature of attractor patterns, variable sensitivity of complex systems to initial conditions, and emergent behaviors in the evolution of a system in its full environmental context over time, could help account for the mixed basic science and controlled clinical trial research findings, in contrast with the consistently positive outcomes of observational studies in the literature. Application of theories and methods from complex systems and network science can open a new era of advances in understanding factors that lead to good versus poor individual global outcome patterns and to rational triage of patients to one type of care over another. The growing reliance on complex systems thinking and systems biology for cancer research affords a unique opportunity to bridge between the CAM and conventional medical worlds with some common language and conceptual models.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".