Amyotrophic lateral sclerosis: Ethical challenges
Bibliographic record
Abstract
# {#article-title-2} In the February 2011 issue of Neurology: Clinical Practice , Johnston et al.1 address the ethical issues amyotrophic lateral sclerosis (ALS) raises in a professional and compassionate manner. Nevertheless, some “unethical” thoughts come to mind. Doctors should not behave with an impersonal attitude, should be compassionate, and should not abandon patients even if faced with unacceptable requests. Is assisted suicide an unacceptable request? All of us have experienced assisting a dying patient with ALS and such a request should be addressed in a very thoughtful way. To withdraw a patient from a ventilator under a palliative sedation is more scientific and probably considered more ethical, while assisted suicide is seen as more sudden and dramatic but perhaps less devastating and less financially burdensome to the family. The economic issue sounds outrageous but insurance companies or private health care facilities are greedy. The patient had a previous tragic experience and instinctively asked for a rapid death to avoid a long-lasting agony. He knew that if he had deferred the issue to a surrogate decision-maker, it … Correspondence to: jableckimd{at}gmail.com Correspondence to: wendy.johnston{at}ualberta.ca
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 | 0.041 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.037 | 0.037 |
| Insufficient payload (model declined to judge) | 0.004 | 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".