PREDICTING ADVERSE OUTCOMES IN ED PATIENTS USING THE CLINICAL FRAILTY SCALE AND A FRAILTY INDEX
Bibliographic record
Abstract
Abstract Our aim was to use the Comprehensive Geriatric Assessment (CGA) database to investigate whether the Clinical Frailty Scale (CFS) measuring the baseline state, and a Frailty Index (FI) based on a CGA (current state, with acute illness) can predict adverse outcomes in acutely ill Emergency Department (ED) patients. It contains CFS and FI scores on 1028 ED patients referred to internal medicine at the Halifax Infirmary between 2009-2019 (Mage 80.69 ± SD 8.28, range 57-103; 54.9% female). The mean scores were 0.44±0.14 (FI) and 5.58±1.66 (CFS). Most patients (72%) arrived via ambulance. The average length of stay was 27.0±20.5 hours. Overall, 22% were discharged home, and 63.5% had died by December 2017 with a mean survival time of 1.98±2.01 years. Controlling for age, sex, and Canadian Triage Acuity Score, the odds ratio (95% Confidence Interval) of being discharged home and the hazard ratio (95% Confidence Interval) for mortality was 0.94 (0.92-0.95) and 1.02 (1.02-1.03), respectively per 0.01-point increase in FI. For the CFS, using score ≤4 as the reference, the odds ratio and the hazard ratio were 0.70 (0.42-1.16) and 2.02 (1.51-2.69), respectively for the CFS 5 group, 0.47 (0.27-0.81) and 2.72 (2.05-3.61), respectively for the CFS 6 group, and 0.38 (0.21-0.70) and 4.67 (3.51-6.20), respectively for the CFS 7-9 group. Even controlling for acuity, both the CFS and the FI independently predict adverse outcomes in ED patients. These add prognostic information to the routinely collected ED assessments, and establish targets for care plan based on recovery to baseline.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".