The Association Between Frailty and a Nurse-Identified Need for Comprehensive Geriatric Assessment Referral from the Emergency Department
Bibliographic record
Abstract
BACKGROUND: Emergency nurses commonly conduct geriatric assessments in the emergency department (ED). However, little is known about what geriatric syndromes or clinical presentations prompt a nurse to document an identified need for comprehensive geriatric assessment (CGA). OBJECTIVES: To examine the association between geriatric syndromes, like frailty, and a nurse-identified need for a CGA following emergency care. METHODS: We conducted a secondary analysis of a multi-province Canadian cohort from the InterRAI Multinational Cohort Study. We collected data at ED registration from patients 75 years of age and older (n = 2,274) from eight ED sites across Canada between November 2009 and April 2012. Geriatric syndromes were assessed by trained emergency nurses using the interRAI ED Contact Assessment; and we retrospectively calculated the ED frailty index. We employed binary logistic regression to determine the adjusted associations between geriatric syndromes and a nurse-identified need for a CGA. RESULTS: Approximately one-quarter (28%) of older adults were identified to need a CGA following emergency care. A 0.1 unit increase in the ED frailty index increased the likelihood of a nurse identify a need for CGA (RD: 6.6; 95% CI = 5.5-7.9). Most geriatric syndromes increased the probability of a nurse documenting the need for a CGA. CONCLUSION: When assessed by emergency nurses, the identified need for CGA is strongly linked to the presence of geriatric syndromes, including frailty. We provide face validity for the continued use of emergency nurses for screening and assessing older ED patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".