Quality of life in ICU survivors from 1991 to 2022: a bibliometric analysis based on CiteSpace
Bibliographic record
Abstract
Abstract Objective: In recent years, the number of ICU survivors has increased year by year, and their health-related quality of life after discharge has been an increasingly concerned. This study aims to analyze the development status, research hotspots, research frontiers, and future development trends of the quality of life of ICU survivors after discharge. Methods: The relevant literature was retrieved from the WOSCC database, including only the articles published in English. CiteSpace6.0R software was used to analyze the collaboration network of countries/regions, institutions, and keywords, and co-citation analysis of references. Results: A total of 1495 related research papers were included in this study. The major countries that conducted the research included the United States (US), Australia, England, Canada, Germany, Netherlands, France, and Italy. The research institutes are mainly located in the United States and France, and the main researchers come from the research institutes in these countries. The most cited authors are Needham D, Hopkins R, Jackson J, and Ely E. The top 3 journals with the largest number of published articles were the Journal of Critical Care Medicine, Journal of Critical Care, and Journal of Intensive Care Medicine. The top 5 most commonly used keywords were cognitive impairment, symptom, critical care, acute kidney injury, long-term outcomes, and mechanical ventilation. Post-intensive care syndrome, ICU survivor, critical care outcome, acute respiratory syndrome, and frailty would be potentially cited frequently over the coming years, which represent the emerging trends. Conclusion: This study demonstrates the global research hotspots and trends of related quality-of-life research in ICU survivors. It can help scholars quickly understand the research status and hot spots in this field.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.149 | 0.175 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".