Clinical Frailty Scale in an Acute Medicine Unit: a Simple Tool That Predicts Length of Stay
Bibliographic record
Abstract
BACKGROUND: Frailty is characterized by increased vulnerability to external stressors. When frail older adults are admitted to hospital, they are at increased risk of adverse events including falls, delirium, and disability. The Clinical Frailty Scale (CFS) is a practical and efficient tool for assessing frailty; however, its ability to predict outcomes has not been well studied within the acute medical service. OBJECTIVE: To examine the CFS in elderly patients admitted to the acute medical ward and its association with length of stay. DESIGN: Prospective cohort study in an acute care university hospital in London, Ontario, Canada, involving 75 patients over age 65, admitted to the general internal medicine clinical teaching units (CTU). MEASUREMENTS: Patient demographics were collected through chart review, and CFS score was assigned to each patient after brief clinician assessment. The CFS ranges from 1 (very fit) to 9 (terminally ill) based on descriptors and pictographs of activity and functional status. The CFS was collapsed into three categories: non-frail (CFS 1-4), mild-to-moderately frail (CFS 5-6), and severely frail (CFS 7-8). Outcomes of length of stay and 90-day readmission were gathered through the LHSC electronic patient record. RESULTS: Severe frailty was associated with longer lengths of stay (Mean = 12.6 ± 12.7 days) compared to mild-to-moderate frailty (mean = 11.2 ± 10.8 days), and non-frailty (mean = 4.1 ± 2.1 days, p = .014). This finding was significant after adjusting for age, sex, and number of medications. Participants with higher frailty scores showed higher readmission rates when compared with those with no frailty (31.2% for severely frail, vs. 34.2% for mild-to-moderately frail vs. 19% for non-frail) although there was no significant difference in the adjusted analysis. CONCLUSION: The CFS helped identify patients that are more likely to have prolonged hospital stays on the acute medical ward. The CFS is an easy to use tool which can detect older adults at high risk of complicated course and longer stay. Objective early identification of seniors with frailty in acute care units can help to target interventions to prevent complications and to implement effective discharge planning in high risk older adults.
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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 | 0.004 |
| 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) | 0.003 | 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".