Resilience is associated with frailty and older age in hospitalised patients
Bibliographic record
Abstract
BACKGROUND: Little is known about resilience in an internal medicine setting. We aimed to assess the relationship between resilience and frailty and other clinical and sociodemographic characteristics in a cohort of prospectively enrolled hospitalised patients. METHODS: In 2017-2019, we consecutively enrolled patients in our internal medicine wards. We selected all patients who filled in the 25-item Connor-Davidson resilience scale (CD-RISC). Mean resilience was evaluated according to baseline demographic (i.e., age, sex, marital and socioeconomic status) and clinical (i.e., Cumulative Illness Rating Scale [CIRS], Edmonton Frail Scale [EFS], Barthel index, Short Blessed test, length of stay [LOS]) data. A multivariable analysis for assessing factors affecting resilience was fitted. RESULTS: Overall, 143 patients (median age 69 years, interquartile range 52-79, 74 females) were included. Resilience was significantly lower in frail (p = 0.010), elderly (p = 0.021), dependent (p = 0.032), and more clinically (p = 0.028) and cognitively compromised patients (p = 0.028), and in those with a low educational status (p = 0.032). No relation between resilience and LOS was noticed (p = 0.597). Frail patients were significantly older (p < 0.001), had a greater disease burden as measured by CIRS comorbidity (p < 0.001) and severity indexes (p < 0.001), were more dependent (p < 0.001), more cognitively impaired (p < 0.001), and displayed a lower educational level (p = 0.011) compared to non-frail patients. At multivariable analysis, frailty (p = 0.022) and dependency (p = 0.031; according to the Barthel index) were associated with lower resilience in the age groups 18-64 and ≥ 65 years, respectively. CONCLUSIONS: Low resilience was associated with frailty and dependency with an age-dependent fashion. Studies assessing the impact of this finding on important health outcomes are needed. TRIAL REGISTRATION: Clinical Complexity in Internal Medicine Wards. San MAtteo Complexity Study (SMAC); NCT03439410 . Registered 01/11/2017.
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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 | 0.001 |
| 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".