The role of illness acuity on the association between frailty and mortality in emergency department patients referred to internal medicine
Bibliographic record
Abstract
BACKGROUND: we investigated whether two frailty tools predicted mortality among emergency department (ED) patients referred to internal medicine and how the level of illness acuity influenced any association between frailty and mortality. METHODS: two tools, embedded in a Comprehensive Geriatric Assessment (CGA), were the clinical frailty scale (CFS) and a 57-item deficit accumulation frailty index (FI-CGA). Illness acuity was assessed using the Canadian Triage and Acuity Scale (CTAS). We examined all-cause 30-day and 6-month mortality and time to death. RESULTS: in 808 ED patients (mean age ± SD 80.8 ± 8.8, 54.4% female), the mean FI-CGA score was 0.44 ± 0.14, and the CFS was 5.6 ± 1.6. A minority (307; 38%) were classified as having high acuity (CTAS: 1-2). The 30-day mortality rate was 17%; this increased to 34% at 6 months. Compared to well patients with low acuity, the risk of 30-day mortality was 22.5 times (95% CI: 9.35-62.12) higher for severely frail patients with high acuity; 53% of people with very severe frailty (CFS = 8) and high acuity died within 30 days. When acuity was low, the risk for 30-day mortality was significantly higher only among those with very high levels of frailty (CFS 7-9, FI-CGA > 0.5). When acuity was high, even lower levels of frailty (CFS 5-6, FI-CGA 0.4-0.5) were associated with higher 30-day mortality. CONCLUSIONS: across levels of frailty, higher acuity increased mortality risk. When acuity was low, the risk was significant only when the degree of frailty was high, whereas when acuity was high, even lower levels of frailty were associated with greater mortality risk.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".