Bibliographic record
Abstract
PURPOSE OF REVIEW: This article provides an update on the recent research and evidence regarding quality in end-of-life (EOL) discussions with a focus on the care of a person with cancer. RECENT FINDINGS: Clinicians have the challenging task of customizing the information exchange that occurs during an EOL discussion. Patients identify important stipulations that accompany a desire for frank EOL discussions. These include timing of the discussion, ensuring evaluation of readiness to engage in the EOL discussion, and being invited to participate. The timing of an EOL discussion is likely to be more important than the setting in which an EOL discussion occurs. Less than 1 month prior to a person's death is likely to be an inadequate amount of time to allow a patient to consider and reflect on his or her EOL preferences. Among those admitted to the hospital, delay in the timing of EOL discussions carries the risk of losing decision-making capacity. SUMMARY: There is greater use of quality metrics as patient outcomes among studies examining EOL discussions. System-wide approaches to improving EOL discussions should include standardized documentation templates that are widely accessible in electronic medical records.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".