The ethics, applications, and contributions of cadaver testing in injury prevention research
Bibliographic record
Abstract
OBJECTIVE: This study aims to establish best practices and guidelines to ensure that experimental research utilizing Postmortem Human Subjects (PMHS) for injury prevention adheres to relevant ethical principles, which are also commonly accepted in research involving human tissues and living subjects. Furthermore, it reviews existing literature to underscore the pivotal role of PMHS testing in evaluating the efficacy of safety systems, with a particular focus on airbag performance. METHODS: This paper conducts an examination of the primary ethical principles governing human subject research as outlined in the Declaration of Helsinki (1965) and traces their evolution up to the latest framework proposed by the Council for International Organizations of Medical Sciences (CIOMS) in 2002. Input was solicited from international experts and laboratories experienced in PMHS testing to understand how these ethical principles are implemented in practice. This is complemented by a comprehensive review of literature that assesses the contribution of PMHS testing to airbag performance enhancements in frontal impacts. RESULTS: The findings underscore the importance of informed consent from donors or their next-of-kin, as highlighted in CIOMS declarations, to ensure the ethical integrity of the donation process in line with international standards. The study also finds it customary for an independent review board to evaluate the research methodology and the necessity of employing PMHS tissue over alternative methods, such as computational models or crash test dummies. Despite various national regulations on human subject participation and living tissue research, no specific legal framework governing PMHS tissue use was identified. The systematic literature review revealed that PMHS testing has been crucial in identifying potential injury mechanisms not detected by Anthropomorphic Test Devices (ATD), significantly contributing to the enhancement of computer human body models and the biofidelity of crash test dummies. CONCLUSION: The International Council on the Biomechanics of Injury (IRCOBI) recognizes the need to provide guidance for research involving human cadaveric tissue to be conducted with the highest ethical standards. This study proposes five recommendations to ensure adherence to these ethical principles in PMHS testing, highlighting the paramount importance of obtaining informed consent and securing independent committee approval. Moreover, IRCOBI emphasizes that until a thorough understanding of tissue damage tolerance levels is achieved and human surrogates, such as ATDs or Human Body Models (HBM), reach full biofidelity, the use of human cadavers remains indispensable for developing effective injury prevention strategies and measures.
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 | 0.404 | 0.504 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".