Quality of life in survivors after a period of hospitalization in the intensive care unit: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To assess the long-term, health-related quality of life of intensive care unit survivors by systematic review. METHODS: The search for, and selection and analysis of, observational studies that assessed the health-related quality of life of intensive care unit survivors in the electronic databases LILACS and MEDLINE® (accessed through PubMed) was performed using the indexed MESH terms "quality of life [MeSH Terms]" AND "critically illness [MeSH Terms]". Studies on adult patients without specific prior diseases published in English in the last 5 years were included in this systematic review. The citations were independently selected by three reviewers. Data were standardly and independently retrieved by two reviewers, and the quality of the studies was assessed using the Newcastle-Ottawa scale. RESULTS: In total, 19 observational cohort and 2 case-control studies of 57,712 critically ill patients were included. The follow-up time of the studies ranged from 6 months to 6 years, and most studies had a 6-month or 1-year follow up. The health-related quality of life was assessed using two generic tools, the EuroQol and the Short Form Health Survey. The overall quality of the studies was low. CONCLUSIONS: Long-term, health-related quality of life is compromised among intensive care unit survivors compared with the corresponding general population. However, it is not significantly affected by the occurrence of sepsis, delirium, and acute kidney injury during intensive care unit admission when compared with that of critically ill patient control groups. High-quality studies are necessary to quantify the health-related quality of life among intensive care unit survivors.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".