186 FRAILTY IN HOSPITALISED OLDER ADULTS AND THEIR SPOUSES: A CROSS-SECTIONAL STUDY
Bibliographic record
Abstract
Abstract Background Hospital admissions are a critical time-point for assessing frailty. Frail older adults have high utilisation of healthcare resources and many require on-going care. Often spouses provide this care, however caregiving can lead to increased frailty in the caregiver. A description of frailty and caregiving within marital dyads has not been explored previously, in relation to hospital admissions. Methods Aim: To measure the frequency and describe the characteristics of frailty in hospitalised older adults and their spouse. Objectives were to: (1) Describe frailty, physical and cognitive performance in patients and spouses; (2) Determine frailty correlations between dyads; (3) Establish if patients are more likely to have spouses of the same frailty classification; (4) Describe caregiving within the context of frailty. Participants were recruited from geriatric and stroke services from October 2019-March 2020. Fried Frailty Phenotype (FFP) was used to test frailty. Clinical Frailty Scale, Grip Strength, Montreal Cognitive Assessment, Timed Up and Go, Geriatric Depression Scale and self-reported: mobility status, caregiving status, number of falls and number of hospital admissions were also assessed to describe the population. Results Data from 27 dyads that participated and consented were analysed using SPSS. A total of 15 patients (55.5%) and four spouses (14.8%) were classified as frail. Of 15 frail patients, four had frail spouses, five had pre-frail spouses and six had robust spouses. No pre-frail or robust patients had frail spouses. Spearman’s Correlational analysis showed no correlation in FFP between patient and spouse (r=0.24, p=0.23). Participants that provided care (44.4%) accounted for 5 patients (frail n=4) and 19 spouses (frail n=4). Conclusion Frailty in dyads is heterogeneous. Caregivers are commonly frail and provide care to frail partners. Older adults admitted to hospital should be assessed for frailty. If the patient has a spouse, especially if the patient is frail, their spouse should be assessed for frailty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".