Medicine at the Bar: Medical Experts, Lawyers, and the Making of Malpractice in the Courtroom
Bibliographic record
Abstract
This dissertation shows, via a detailed analysis of trial proceedings, how medical malpractice is made in the courtroom. To show this I build on insights from work in the fields of philosophy, history, and sociology of medicine and science. I draw these insights together into a general, rhetorical perspective on the evidentiary process that takes place at trial. This perspective is then deployed in a case study of a single trial. Throughout the dissertation I focus on knowledge of the medical standard of care: the norm against which the conduct of physician–defendants is compared to determine whether or not malpractice occurred. Knowledge of the standard is of a particular kind. In many cases, this knowledge cannot be separated from the argumentative work that takes place during the trial. Furthermore, knowledge of the past events themselves—i.e. physician–defendants’ past actions in the situations in which they found themselves—cannot be separated from said argumentative work. The purview of this argumentative work is extensive, since it also includes authorizing certain people—medical expert witnesses—to opine regarding the events. The case study allows me to underscore the extent to which the trial is not only an argumentative practice, but also a highly disciplined and particularized inquiry into past events. Thus the standard of care is a product of the trial qua argumentative, disciplined, and particularized inquiry. This challenges prevailing conceptions of the standard of care in the doctrine and jurisprudence. Following these, the standard of care is akin to a fact that exists independently of the trial, and the trial is merely a means to make this fact accessible to the judge or jury with the least amount of distortion. I argue, contra these conceptions, that the trial has value in itself because it yields knowledge that cannot be gained any other way. In the case of the medical standard of care, it is knowledge made up of heterogeneous considerations—technical, scientific, moral, and legal—brought to bear on specific actions in specific situations.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.023 | 0.050 |
| Scholarly communication | 0.029 | 0.028 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".