From hospital to home: A heightened window of vulnerability post-critical illness
Bibliographic record
Abstract
BACKGROUND: Healthcare innovations have not kept pace with the burden of critical illness survivorship. The majority of patients treated in an intensive care unit (ICU) will survive but suffer new or worsening physical, cognitive or mental health sequelae, known as post-intensive care syndrome (PICS). For these survivors, the transition from hospital-based acute care to community-based care is often complex, with high rates of emergency department visits and unplanned hospital readmission. The purpose of this analysis is to describe ICU survivor and family caregiver experiences navigating the challenges in the transition from hospital to home. METHODS: In this qualitative interpretive description study, data from semi-structured interviews with ICU survivors and family caregivers in the months following discharge from the hospital to home were analyzed using thematic and constant comparative methods. RESULTS: The 47 study participants included 28 survivors (mean age 58, 17 men and 11 women) and 19 family caregivers (mean age 53, 6 men and 13 women), who represented 32 cases. The challenges experienced when transitioning from hospital included (1) feeling too ill to go home and pushed out of the hospital without a plan, (2) confronting illness and exhaustion without a safety net, and (3) managing at home with inadequate healthcare. During this time, patients were vulnerable to stagnation or deterioration of their mental and physical health, unmet healthcare needs, and unplanned emergency department visits and rehospitalization. CONCLUSIONS: The challenging transition from the hospital setting suggests a heightened window of vulnerability in the initial months post-discharge and emphasizes a crucial missing middle in our healthcare system, leaving vulnerable patients at risk for ongoing and new health problems.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".