THE ASSOCIATION OF A FRAILTY INDEX AND INCIDENT DELIRIUM IN HOSPITALIZED VETERANS
Bibliographic record
Abstract
Abstract Frailty is an accumulation of deficits that helps identify patients who are vulnerable to stressors. Acute illness and hospitalization are stressors that may result in delirium. Delirium is significant in older adults, resulting in increased hospital stays, institutionalization, morbidity, and mortality. This study aimed to determine if a frailty index (FI), calculated on hospital admission, was associated with the development of incident delirium. An FI was built on an accumulation of deficits model which included assessments of cognition, physical function, and medical comorbidities for a cohort of 218 patients admitted to a Veteran Affairs medical facility. The FI was calculated as a proportion of possible deficits (range 0-1; higher scores indicate increased frailty). Delirium was assessed daily by expert clinician interview. Participants were, on average, 71 years (SD=9.53), white (92.7%), and male (91.7%). Participants were grouped using FI ranges as non-frail (FI<0.25; 26%), pre-frail (FI=0.25-0.35; 39%), and frail (FI>0.35; 35%). Incident delirium was more likely to occur in those who were frail (29.3%, p=0.001), compared to those who were pre-frail (20.9%) or non-frail (3.6%). The association of FI and incident delirium remained after adjustment for age, education, and other demographics (pre-frail: adjusted OR=5.64, 95%CI; 1.23, 25.99; frail: adjusted OR=6.80, 95%CI; 1.38, 33.45). Continued data analysis will include an AUC model to demonstrate robustness of the FI. The results from this study support the use of frailty assessments at hospital admission to identify patients at high risk of delirium and in need of additional clinical support and interdisciplinary resources.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".