Using Tort Law to Secure Patient Dignity
Bibliographic record
Abstract
The practice of using anesthetized patients to teach pelvic exams on female patients in university hospitals has been well documented for years. A 1992 study showed that 37 percent of U.S. and Canadian medical schools allowed students to use anesthetized women without their consent to learn how to perform pelvic exams. Anecdotal accounts in the U.S. confirm that men are not immune from such indignities. Although patients have been unable, thus, far to enforce their own interests and protect their dignity, the tort system may yet succeed in securing the right of patients to decide who touches their bodies and under what circumstances. Using tort law to secure patient dignity examines the theories of recovery available to those who have been the subject of unauthorized teaching exams. It evaluates a patient's likely success under theories of medical battery and malpractice, failure to obtain informed consent, and breach of fiduciary duty. It explores the obstacles to recovery and the arguments that will be raised by physicians and teaching hospitals in defense of this practice, and concludes that the tort system may be the most effective vehicle to redress the unauthorized use of patients' bodies as teaching tools and to curb this practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.016 | 0.007 |
| 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".