Communicating to Patients and Families About Post-Intensive Care Syndrome
Bibliographic record
Abstract
Millions of people around the world survive critical illness each year only to realize that they and their loved ones are grappling with a new "normal" after hospital discharge for which their medical team may not have adequately prepared them. Up to one-half of all ICU survivors suffer from new or worsening impairments in physical, cognitive, and psychological domains of health that are often not realized until they attempt to re-enter their previous lives. These devastating long-term sequelae of critical illness, collectively described as post-intensive care syndrome (PICS), can carry enormous consequences for an ICU survivor's ability to care for their family, return to work, and regain their previous quality of life for months to years after their inciting illness. Despite mounting research on PICS and survivorship, a knowledge gap exists whereby ICU team members may not always be aware of PICS and may not counsel their patients on the challenges awaiting them after discharge. Understanding how best to communicate these challenges to patients and families is crucial in preparing for survivorship beyond the ICU. In this review, we summarize PICS and possible recovery trajectories of ICU survivors. We then discuss communication strategies, emphasizing the role of empathy. Finally, we provide a suggested framework to handle these crucial conversations. We aim to equip clinicians with the knowledge and framework to care for a patient who has survived critical illness but now faces the possibility of struggles inadequately addressed by our health care system.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".