Prolonging life: legal, ethical, and social dilemmas
Bibliographic record
Abstract
The ability of modern medicine to prolong life has raised a variety of difficult legal, ethical, and social issues on which reasonable minds can differ. Among these are the morality of euthanasia in cases of deep coma or irreversible injury, as well as the Dead Donor Rule with respect to organ harvesting and transplants. As science continues to refine and develop lifesaving technologies, questions remain as to how much medical effort and financial resources should be expended to prolong the lives of patients suspended between life and death. At what point should death be considered irreversible? What criteria should be used to determine when to withhold or withdraw life-prolonging treatments in cases of severe brain damage and terminal illness? To explore these complex dilemmas, Steve Paulson, executive producer and host of To the Best of Our Knowledge, moderated a discussion panel. Pediatrician Sam Shemie, hospice medical director Christopher P. Comfort, bioethicist Mildred Z. Solomon, and attorney Barbara Coombs Lee examined the underlying assumptions and considerations that ultimately shape individual and societal decisions surrounding these issues. The following is an edited transcript of the discussion that occurred November 12, 2013, 7:00-8:30 PM, at the New York Academy of Sciences in New York City.
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 | 0.067 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.038 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.034 | 0.046 |
| 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".