Legal and ethical issues in the regulation and development of engineering achievements in medical technology: A 2006 perspective
Bibliographic record
Abstract
Two papers, Legal and Ethical Issues in the Regulation and Development of Engineering Achievements in Medical Technology parts I and II were written in 1990 by three authors of diverse backgrounds and published in the IEEE Engineering in Medicine and Biology Magazine in March of the same year. Part I of the paper discusses the existing Food and Drug Administration (FDA) requirements that existed in 1990 to regulate the clinical trial process for medical devices, obtaining Marketing Approval and exceptions that may allow the use of unapproved devices. The paper discusses how the FDA has loosened some of the stringent regulations to further its goal of encouraging new development while protecting public health and maintaining ethical standards. Part H of the paper focuses on the ethical implications of the process of introducing a new technology to the market place, specifically in the usage of unapproved technologies for emergency use and feasibility studies. This paper discusses the topics covered in the two papers and the changes that have been made to the FDA guidelines since their publication in 1990.
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 | 0.081 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.071 |
| Scholarly communication | 0.031 | 0.021 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.051 | 0.036 |
| Insufficient payload (model declined to judge) | 0.002 | 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".