Bibliographic record
Abstract
As medical science continues to advance, patients nowadays with progressive cardiopulmonary diseases live to older ages. However, they too will eventually reach their unsustainable physiological limit and many die in poor health and discomfort prior to their demise. Regrettably many physicians have not kept pace in dealing with the inevitable end-of- life issues, along with modern technological developments. Without proper guidance, ill-informed patients often face unnecessary anxiety, receive futile resuscitation at the expense of their dignity and public cost which has and will become increasingly overwhelming according to our current demographic trends. In any health care reform, experts often suggest that difficult questions will have to be asked but the solutions are at least partly in the logistical details. From time to time, we see an isolated "Do Not Resuscitate" or DNR order in the chart, which is not always followed by thoughtful discussion on the boundary of care, either simultaneously or known to be followed up soon. This paper attempts to begin asking some of these difficult questions, point out the fallacies of this order and expose the weaknesses in the present state of entitlement by public demand if physicians retreats more from the discussion. The solution does not lie in asking the questions but in changing the practice pattern in real life on a continuous basis, hopefully to be eventually accepted by most, if not all.
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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".