Bibliographic record
Abstract
The end of life is a difficult moment for patients who are suffering both physically and mentally. Many are requesting euthanasia when they are suffering too much, but this is not easy to obtain, as there are many pros and cons within the medical community and the society regarding euthanasia. As a result, many dying patients will not receive euthanasia even though they strongly request it. Helping patients to die when they are suffering too much should be considered part of good care. Fortunately, we also have palliative care that helps patient manage pain until they die. Euthanasia and palliative care respects the principle of patient dignity, which does not depend on the patient’s usefulness to the society. Instead, human dignity is based on the inherent worth of each human; a value that must not be influenced by external factors. Being human confers a dignity and the right to choose to continue to live with pain or to die in peace; it is not control, but acceptance. It is important to provide people who suffer with the compassion and assistance they need in order to move them toward acceptance until the time of death. The last theme of this chapter discusses the role of cannabis as a good alternative for patients suffering from chemotherapy-induced nausea and vomiting, cancer-associated pain, anorexia and cachexia syndrome, insomnia, depression, and anxiety. However, we recommend the medical-based cannabis for better quality, though the prescription should be performed on a case-by-case basis by a qualified physician.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".