Stop that Train! I Want to Get Off: Emergency Care for Patients with Advanced Dementia
Bibliographic record
Abstract
The prevalence of advanced dementia (AD) is expected to increase dramatically over the next few decades. Patients with AD suffer from recurrent episodic illnesses that frequently result in transfers to acute care hospitals. The default pathway followed by some emergency physicians, internists and intensivists who see those patients is to prioritize disease-directed therapies over attention to the larger picture of AD. While this strategy is desired by many families, some families prefer a different approach. This essay examines the reason why there can be a failure to focus on the over-arching issue of AD and offers suggestions for improvement. Gaps in information and physician workload are important factors, but we argue that until physicians who see patients in emergency departments learn to pause first and ask “Why are we doing this?” they will revert to their comfort zone of ordering tests and therapies that may be unwanted. A separate emergency palliative care pathway may be one solution. Shifting the focus back to the larger picture of AD and away from the physiologic disturbance of the moment may alter the trajectory of care in ways that truly respect the wishes of some patients and their families.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| 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".