Personalized Medicine and Complementary and Alternative Medicine: In Search of Common Grounds
Bibliographic record
Abstract
OBJECTIVES: As complementary and alternative medicine (CAM) continues to assume influence in medical care delivery, biomedical orthodoxy has contemporaneously experienced landmark technoscientific advances, tempting analysts to question the relevance of CAM to 21st century medical provision. This article focuses on one representation of contemporary advances in biomedicine, namely, the phenomenon of personalized medicine (PM) and the technoscientific contexts for its evolution. It examines whether biomedicine's embrace of the PM concept widens the conceptual and philosophical gulf between it and CAM. DESIGN: Focusing on genomics and its translation into PM, the article finds that presently, the gene-environment dynamic is an important aspect of genomics and PM. However, there is a lopsided emphasis in the gene-environment matrix that focuses on toxicogenomics (i.e., the effect of toxins and chemicals, including drugs on genes and genetic materials). This approach to genomics ignores the role of other environmental stressors, which constitute components of an individual's health experience critical to PM. CONCLUSIONS: If this lopsided approach is addressed, in a counterintuitive way, PM has potential for engendering a confluence between biomedicine and CAM as a part of the paradox of the 21st century medical landscape.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.076 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".