End-of-Life Decision Making: How Patients, Substitutes, and Physicians Make Decisions
Bibliographic record
Abstract
Abstract This tool was created to address a perceived gap in the education of our postgraduate internal medicine trainees around the practical aspects of end-of-life decision making. Based on an extensive up-to-date literature review of the topic, the PowerPoint presentation outlines several facets of end-of-life decision making: (1) components of these decisions and generally accepted definitions, (2) factors affecting patients' decisions, (3) substitute decision-maker accuracy, (4) physician-level factors affecting decision making, and (5) implications for everyday practice. The presentation is intended to be a springboard for an interactive discussion of experiences with end-of-life decisions and is best suited to an audience that has some experience with these situations (e.g., medical or surgical residents, ICU fellows, etc.). The session is 2 hours in duration, with a 10-minute break in the middle. It is possible to reduce the session to just 1 hour, but this will potentially curtail some of the discussion, which is likely to be the most stimulating and highest-rated component of the session. This session is unique in that it provides a forum for discussion, as well as an overview of the current state of the art in the factors known to influence end-of-life decision making. It has been presented as a 1-hour round for medical residents and students in two Toronto teaching hospitals. Although not formally evaluated, it was anecdotally highly rated by trainees at all levels.
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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".