Rethinking Clinical Trials and Personalized Medicine with Placebogenomics and Placebo Dose
Bibliographic record
Abstract
Pharmacogenomics, nutrigenomics, vaccinomics, and the nascent field of plant omics are examples of variability science. They are embedded within an overarching framework of personalized medicine. Across these public health specialties, the significance and biology of the placebo response have been historically neglected. A placebo is any substance such as a sugar pill administered in the guise of medication, but one that does not have pharmacological activity. Placebos do have clinical effects, however, that can be substantive in magnitude and vary markedly from person-to-person depending, for example, on the type of disease, symptoms, or clinical trial design. Research over the past several decades attests to a genuine neurobiological basis for placebo effects. All drugs have placebo components that contribute to their overall treatment effect. Placebos are used in clinical trials as control groups to ascertain the net pharmacological effect of a drug candidate. Not only less well known but also relevant to rational therapeutics and personalized medicine is the nocebo. A nocebo effect occurs when an inert substance is administered in a context that induces negative expectations, worsening patients' symptoms. With the COVID-19 pandemic, there are high public expectations for new vaccines and medicines to end the contagion, while at the same time antiscience, post-truth, and antivaccine movements are worrisomely on the rise. These social movements, changes in public health cultures, and conditioned behavioral responses can trigger both placebo and nocebo effects. Hence, in clinical trials, forecasting and explaining placebo and nocebo variability are more important than ever for robust science and personalized health care. Against this overarching context, this article provides (1) a brief history of placebo and (2) a discussion on biology, mechanisms, and variability of placebo effects, and (3) discusses three emerging new concepts: placebogenomics, nocebogenomics, and augmented placebo, that is, the notion of a "placebo dose." We conclude with a roadmap for placebogenomics, its synergies with the nascent field of social pharmacology, and the ways in which a new taxonomy of drug and placebo variability can be anticipated in the next decade.
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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 | 0.355 | 0.435 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.033 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.015 | 0.034 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".