Comparing three different measures of frailty in medical inpatients: Multicenter prospective cohort study examining 30‐day risk of readmission or death
Bibliographic record
Abstract
BACKGROUND: Multiple tools are used to identify frailty. OBJECTIVE: To compare the global Clinical Frailty Scale (CFS) with more objective phenotypic tools (modified Fried score and the Timed Up and Go Test [TUGT]). DESIGN: Prospective cohort study. SETTING: General medical wards in Edmonton, Canada. PARTICIPANTS: Adults being discharged back to the community. MEASUREMENTS: All frailty assessments were done within 24 hours of discharge. Patients were classified as frail if they scored ≥5 on the CFS and/or ≥3 on the modified Fried score, and/or had reduced mobility (>20 seconds on the TUGT). The main outcome was readmission or death within 30 days. RESULTS: Of 495 patients, 211 (43%) were frail according to at least 1 assessment, 46 (9%) met all 3 frailty definitions, and 17% died or were readmitted to the hospital within 30 days. Although patients classified as frail on the CFS exhibited significantly higher 30-day readmission/death rates (23% vs 14% for not frail, P = 0.005; 28% vs. 12% in the elderly, P < 0.001), even after adjusting for age and sex (adjusted odds ratio [aOR]: 2.02, 95% confidence interval [CI]: 1.19-3.41 for all adults; aOR: 3.20, 95% CI: 1.55-6.60 for the elderly), patients meeting either of the phenotypic definitions for frailty but not the CFS definition were not at higher risk of 30-day readmission/death (aOR: 0.87, 95% CI: 0.34-2.19 for all adults and aOR: 1.41, 95% CI: 0.72-2.78 for the elderly). CONCLUSIONS: Frailty has a significant impact on postdischarge outcomes, and the CFS is the most useful of the frequently used frailty tools for predicting poor outcomes after discharge. Journal of Hospital Medicine 2016;11:556-562. © 2016 Society of Hospital Medicine.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".