Association between frailty and 30-day outcomes after discharge from hospital
Bibliographic record
Abstract
BACKGROUND: Readmissions after hospital discharge are common and costly, but prediction models are poor at identifying patients at high risk of readmission. We evaluated the impact of frailty on readmission or death within 30 days after discharge from general internal medicine wards. METHODS: We prospectively enrolled patients discharged from 7 medical wards at 2 teaching hospitals in Edmonton. Frailty was defined by means of the previously validated Clinical Frailty Scale. The primary outcome was the composite of readmission or death within 30 days after discharge. RESULTS: Of the 495 patients included in the study, 162 (33%) met the definition of frailty: 91 (18%) had mild, 60 (12%) had moderate, and 11 (2%) had severe frailty. Frail patients were older, had more comorbidities, lower quality of life, and higher LACE scores at discharge than those who were not frail. The composite of 30-day readmission or death was higher among frail than among nonfrail patients (39 [24.1%] v. 46 [13.8%]). Although frailty added additional prognostic information to predictive models that included age, sex and LACE score, only moderate to severe frailty (31.0% event rate) was an independent risk factor for readmission or death (adjusted odds ratio 2.19, 95% confidence interval 1.12-4.24). INTERPRETATION: Frailty was common and associated with a substantially increased risk of early readmission or death after discharge from medical wards. The Clinical Frailty Scale could be useful in identifying high-risk patients being discharged from general internal medicine wards.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".