A Common Law Prescription for a Medical Malaise
Bibliographic record
Abstract
The Medical Methods Exception (MME) has always seemed remarkable. It presently excludes technologies applied on or in the body from patentability, claiming to protect medical practitioners treating patients from liability for patent infringement. The exception’s existence seems to argue that the medical profession’s work is significant enough to society to warrant special rules in patent law. In so doing, the MME contradicts the technology-neutral, morally agnostic stance of patent law which leaves the regulation of polycentric disputes to other fora. More remarkable, however, is how the MME became an integral part of patent law doctrine and now the European Patent Convention (EPC), and what that process tells us about the common law. Its evolution demonstrates that the common law is not monolithic and self-contained, but relies on mixed legal and professional communities of reception to shape its content. These communities may transcend national boundaries. As a result, informal common law principles may reveal themselves to be better rooted than the seemingly clearer and more certain statutory sources of law, which thus become secondary. This chapter considers the origins of the common law rule followed by the nature and international dimensions of the legal and medical community that received and practised the rule, in particular of Commonwealth courts and Canadian physicians. I will illustrate through the history of the MME the hybrid nature of the common law as part rule and part custom, inaccurately modeled by either legal positivism or as merely the reception of customary norms.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.086 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".