Demographic Changes in Intensive Care Units in Korea over the Last Decade and Outcomes of Elderly Patients: A Single-Center Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Aging is a significant issue worldwide, and Korea is one of the most rapidly aging countries. Along with the demographic transition, the age structure of intensive care unit (ICU) patients changes as well. METHODS: The aim of this study was to analyze the change in age distribution of the ICU patients over the last 10 years and its effect on clinical outcomes. Single-center, retrospective analysis of all patients aged ≥18 years admitted to either the medical or surgical ICU at St. Paul's Hospital, The Catholic University of Korea, between January 2005 and December 2014 was conducted. For clinical outcome, in-hospital mortality, duration of ICU stay, and hospital stay were analyzed. Cost analysis was performed to show the economic burden of each age strata. RESULTS: A total of 10,366 ICU patients were admitted to the chosen ICUs during the study period. The proportion of elderly patients aged ≥65 years increased from 47.9% in 2005 to 63.7% in 2014, and the proportion of the very elderly patients aged ≥80 years increased from 12.8% to 20.7%. However, this increased proportion of elderly patients did not lead to increased in-hospital mortality. The percent of ICU treatment days attributable to elderly patients increased from 51.1% in year 2005 to 64.0% in 2014. The elderly ICU patients were associated with higher in-hospital mortality compared to younger age groups. CONCLUSIONS: The proportion of elderly patients admitted to ICUs increased over the last decade. However, overall in-hospital mortality has not increased during the same period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".