Bibliographic record
Abstract
The increasing availability of physician-assisted death (PAD) has opened up a novel means of making donated bodies available for anatomical dissection. This practice has come to the fore in Canada, but is unlikely to be confined to that country as legislation changes in other countries. The ethical considerations raised by this development are placed within the framework of the ethical guidelines on body donation promulgated by the International Federation of Associations of Anatomists. The discussion centers on understanding the ethical dimensions of moral complicity, and whether it is accepted or rejected. If rejected it is possible to separate ethical concerns regarding PAD from subsequent use of donated bodies, as long as there is fully informed consent and complete ethical and procedural separation of the two. Openness about the origin of bodies for dissection is essential. Students should be instructed on the nuances of moral complicity, and consideration be given to those with moral doubts about PAD. Two issues are raised in considering whether these moves represent an ethical slippery slope: the attraction represented by obtaining relatively "high quality" bodies, and the manner in which organ donation following PAD has led to challenges to the dead donor rule. Although body donation raises fewer concerns, the ethical dimensions of the two are similar. The ethical constraints outlined here have the capacity to prevent an ethical slippery slope and constitute a sound basis for addressing an innovative opportunity for anatomists.
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 | 0.048 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.060 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.021 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".