Mortality in Relation to Frailty in Patients Admitted to a Specialized Geriatric Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: In older adults admitted to intensive care units (ICUs), frailty influences prognosis. We examined the relationship between the frailty index (FI) based on deficit accumulation and early and late survival. METHODS: Older patients (≥65 years) admitted to a specialized geriatric ICU at the Liuhuaqiao Hospital, Guangzhou, China between July-December 2011 (n = 155; age 82.7±7.1 y; 87.1% men) were followed for 300 days. The FI was calculated as the proportion present of 52 health deficits. FI performance was compared with that of several prognostic scores. RESULTS: The 90-day death rate was 38.7% (n = 60; 27 died within 30 days). The FI score was correlated with the Glasgow Coma Scale, Karnofsky Scale, Palliative Performance Scale, Acute Physiology Score-APACHE II and APACHE IV (r (2) = 0.52 to 0.72, p < 0.001). Patients who died within 30 days had higher mean FI scores (0.41±0.11) than those who survived to 300 days (0.22±0.11; F = 38.91, p < 0.001). Each 1% increase in the FI from the previous level was associated with an 11% increase in the 30-day mortality risk (95% CI: 7%-15%) adjusting for age, sex, and the prognostic scores. The FI discriminated patients who died in 30 days from those who survived with moderately high accuracy (AUC = 0.89±0.03). No one with an FI score >0.46 survived past 90 days. CONCLUSION: ICU survival was strongly associated with the level of frailty at admission. An FI based on health deficit accumulation may help improve critical care outcome prediction in older adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".