How Do We Know What is the Best Medicine? From Laughter to the Limits of Biomedical Knowledge
Bibliographic record
Abstract
Medicine has been called a science, as well as an art or a craft, among other terms that express aspects of its practical nature. Medicine is not the abstract pursuit of knowledge. Medical researchers and clinical practitioners aim primarily to help people. As a first approximation then, given its practical focus on the person, the most important question in medicine is: what works?\tTo answer that question, however, we need to understand how we know what works. What are the standards, methods and limits of medical knowledge? That is the central focus and subject of this inquiry: how we know what works in medicine.\n\tTo explore medical knowledge and its limits, this thesis examines the common notion that laughter is the best medicine. Focusing on laughter provides a robust case study of how we know what works in medicine; it also, in part, reveals the thin, perhaps even non-existent, distinction in medicine between empirically-grounded knowledge and intuition.\n\tAs there is no single academic discipline devoted to laughter in medicine, the first chapter situates and charts the course of this unusual project and explains why inquiry into laughter in medicine matters. In the following chapters, we encounter claims from distinguished sources that laughter and humor are the best medicine. These claims are examined from a variety of perspectives including not only the orthodox view of evidence-based medicine, but also from narrative, evolutionary and complexity views of medicine. The rarely explored serious negative side of laughter is also examined. No view provides a firm foundation for belief in laughter medicine. \n\tA general conclusion from this inquiry is that none of the approaches effectively tame the complexity of medical phenomena; indeed each starkly reveals a greater complexity than found at first glance. A narrower conclusion is that providing a basis for claims about laughter in medicine poses its own specific challenges. A third conclusion is that, as things stand, none of the existing approaches seems up to the task of determining whether something such as laughter is the best medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".