Transforming Communication on Serious Illness and Frailty: A Comprehensive Approach to Empowering Informed Decision-Making
Bibliographic record
Abstract
Health care professionals can enhance conversations about serious illness and medical decision-making by adopting a transparent, standardized approach. This article critiques established communication strategies, which often emphasize patient values and goals without providing the necessary medical information to align these goals with a shared understanding of prognosis. We propose an alternate strategy that (1) provides detailed explanations of medical conditions at the beginning of the conversation, (2) includes support persons in discussions, (3) considers capacity, and (4) offers tailored advice by clinicians. The proposed framework aims to provide patients (or their delegates) with the information they need to integrate their values in pursuit of well-informed medical decisions. This strategy builds trust by providing honest information about medical conditions and their trajectories. It empowers decision makers to consider realistic outcomes, allowing them to accept or reject treatments in accordance with their preferences. This article presents a thorough step-by-step guide on how to conduct a serious illness conversation and facilitate medical decision-making, including a supplement that provides example phrases for use in clinical practice.
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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 | 0.049 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".