Identification of older adults with frailty in the Emergency Department using a frailty index: results from a multinational study
Bibliographic record
Abstract
Objective: frailty is a central concept in geriatric medicine, yet its utility in the Emergency Department (ED) is not well understood nor well utilised. Our objectives were to develop an ED frailty index (FI-ED), using the Rockwood cumulative deficits model and to evaluate its association with adverse outcomes. Method: this was a large multinational prospective cohort study using data from the interRAI Multinational Emergency Department Study. The FI-ED was developed from the Canadian cohort and validated in the multinational cohort. All patients aged ≥75 years presenting to an ED were included. The FI-ED was created using 24 variables included in the interRAI ED-Contact Assessment tool. Results: there were 2,153 participants in the Canadian cohort and 1,750 in the multinational cohort. The distribution of the FI-ED was similar to previous frailty indices. The mean FI-ED was 0.26 (Canadian cohort) and 0.32 (multinational cohort) and the 99th percentile was 0.71 and 0.81, respectively. In the Canadian cohort, a 0.1 unit increase in the FI-ED was significantly associated with admission (odds ratio (OR) = 1.43 [95% CI: 1.34-1.52]); death at 28 days (OR = 1.55 [1.38-1.73]); prolonged hospital stay (OR = 1.37 [1.22-1.54]); discharge to long-term care (OR = 1.30 [1.16-1.47]); and need for Comprehensive geriatric Assessment (OR = 1.51 [1.41-1.60]). The multinational cohort showed similar associations. Conclusion: the FI-ED conformed to characteristics previously reported. A FI, developed and validated from a brief geriatric assessment tool could be used to identify ED patients at higher risk of adverse events.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".