Ethical Decision-Making in Biomedical Engineering Research
Bibliographic record
Abstract
To be ethical and professional are terms that are synonymous with being an engineer. The work of engineers frequently affects public safety and health, and can influence business, and even politics. Professional Engineering Associations provide ethical guidelines so that engineers will know how to avoid misconduct, negligence, incompetence, and corruption, which could lead to formal complaints and discipline. Knowledge about ethical decision-making guides engineers facing complex and difficult moral dilemmas (Andrews, 2005, pp. 46). Biomedical engineers doing research and development will undoubtedly be involved in projects that impact humans and/or animals, and thus must be informed on all aspects of ethics that guide such research. They should be particularly aware of the specific guidelines of the institution where the work is to be carried-out and be familiar with the application process to obtain a certificate, allowing the research to proceed. There is clearly a need to guide biomedical engineering students and practitioners in performing a balanced analysis of difficult questions and issues, while respecting societal values that may differ greatly from their own (Frize, 1996; Frize, 2005; Saha & Saha, 1997; Wueste, 1997). There exists a number of articles discussing biomedical engineering and ethics specifically aimed at clinical engineers (Goodman, 1989; Saha & Saha, 1986). These are helpful readings for anyone involved in biomedical research or clinical engineering.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".