Comparison of two electronic medical record-based frailty assessment tools and their association with adverse outcomes in older hospitalized patients with urgent admissions
Bibliographic record
Abstract
BACKGROUND: Frailty assessment is crucial for predicting outcomes in acute care settings; however, its application remains challenging. Therefore, this study aims to evaluate and compare two electronic medical record-based tools-the Canadian Institute for Health Information Hospital Frailty Risk Measure (CIHI-HFRM) and the United Kingdom Hospital Frailty Risk Score (UK-HFRS)-in older patients requiring urgent admission. METHODS: In this retrospective cohort study, we analyzed 35,564 patients aged 65 or older from the MIMIC-IV 2.0 database. Frailty was assessed using CIHI-HFRM and UK-HFRS. Primary outcomes included in-hospital mortality, one-year post-discharge mortality, post-discharge care needs, timely hospital discharge, and one-year readmission rates. Logistic regression, Cox regression, and competing risk models were used for analysis. RESULTS: The CIHI-HFRM and UK-HFRS were significantly associated with in-hospital mortality [odds ratio (OR) per point: CIHI-HFRM 1.10 (95% confidence interval (CI) 1.07-1.13); UK-HFRS 1.06 (95% CI 1.05-1.07)] and one-year post-discharge mortality [hazard ratio (HR) per point: CIHI-HFRM 1.08 (95% CI 1.06-1.09); UK-HFRS 1.05 (95% CI 1.04-1.05)]. Both measures were associated with prolonged hospital stays and post-discharge care needs, while only CIHI-HFRM was linked to one-year readmission risk. CONCLUSION: The CIHI-HFRM and UK-HFRS effectively stratify adverse outcomes risk in older patients requiring urgent admission. They may be considered alongside traditional measures as part of a pragmatic multimodal pathway, which represents a potential direction for clinical application.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".