Frailty as a Predictor of Hospitalization and Low Quality of Life in Geriatric Patients at an Internal Medicine Outpatient Clinic: A Cross-Sectional Study
Bibliographic record
Abstract
Frailty is an aging-associated state that increases patients’ vulnerability to disease, and can lead to various adverse outcomes. It is classified as either physical frailty alone or physical frailty in combination with cognitive impairment (cognitive frailty). There are currently limited data available regarding the prevalence and adverse outcomes of frailty in Thailand. This was a cross-sectional study aimed at determining the prevalence of physical and cognitive frailty and their effects on hospitalization and quality of life. Participants were older patients who attended an internal medicine outpatient clinic. Frailty was diagnosed using the Thai Frailty Index. The Thai version of the MoCA was used to evaluate cognitive status. Univariate and multivariate analyses were performed to compare adverse outcomes in terms of poor quality of life and history of admission to hospital between patients with frailty and non-frail patients, and among patients with physical frailty, cognitive frailty, cognitive impairment, and robust (non-frail and non-cognitively impaired) patients. We enrolled 198 participants. The prevalence of physical and cognitive frailty was 28.78% and 20.70%, respectively. When compared with non-frail patients, frailty was associated with hospitalization (adjusted OR 3.01, p = 0.002) but was not significantly related to quality of life (adjusted OR = 1.98, p = 0.09). However, physical and cognitive frailty were associated with fair quality of life when compared with normal patients (adjusted OR = 4.34, p = 0.04 and adjusted OR = 4.28, p = 0.03, respectively). The prevalence of frailty—particularly cognitive frailty—was high. Frailty was associated with adverse outcomes in terms of hospitalization and quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".