The risk of adverse outcomes in hospitalized older patients in relation to a frailty index based on a comprehensive geriatric assessment
Bibliographic record
Abstract
BACKGROUND: prognostication for frail older adults is complex, especially when they become seriously ill. OBJECTIVES: to test the measurement properties, especially the predictive validity, of a frailty index based on a comprehensive geriatric assessment (FI-CGA) in an acute care setting in relation to the risk of death, length of stay and discharge destination. DESIGN AND SETTING: prospective cohort study. Inpatient medical units in a teaching, acute care hospital. SUBJECTS: individuals on inpatient medical units in a hospital, n = 752, aged 75+ years, were evaluated on their first hospital day; to test reliability, a subsample (n = 231) was seen again on Day 3. MEASUREMENTS: all frailty data collected routinely as part of a CGA were used to create the FI-CGA. Mortality data were reviewed from hospital records, claims data, Social Security Death Index and interviews with Discharge Managers. RESULTS: thirty-day mortality was 93 (12.4%; 95% confidence interval (CI) = 10-15%) of whom 52 died in hospital. The risk of dying increased with each 0.01 increment in the FI-CGA: hazard ratio (HR) = 1.05, (95% CI = 1.04-1.07). People who were discharged home had the lowest admitting mean FI-CGA = 0.38 (±standard deviation 0.11) compared with those who died, FI-CGA = 0.51 (±0.12) or were discharged to nursing home, FI-CGA = 0.49 (±0.11). Likewise, increasing FI-CGA values on admission were significantly associated with a longer length of hospital stay. CONCLUSIONS: frailty, measured by the FI-CGA, was independently associated with a higher risk of death and other adverse outcomes in older people admitted to an acute care hospital.
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 | 0.009 |
| 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 | 0.001 | 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".