Trajectories and Predictors of Functional Capacity Decline in Older Adults From a Brazilian Northeastern Hospital
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Older adults face increased risk of loss of functional capacity both before and during hospitalization, so identifying older adults at risk for loss in functional capacity during hospitalization would help researchers and clinicians make informed decisions. This study aims to evaluate functional changes from preadmission (baseline) until discharge of hospitalized older adults and identify predictors of loss in functional capacity. METHODS: This is a prospective cohort study conducted at a tertiary care hospital in Natal, Brazil, and enrolled all consecutive patients aged 60 years and older between January 1, 2014, and April 30, 2015. Independent variables included personal characteristics, instrumental activities of daily living (IADL) (evaluated by the Lawton and Brody scale), cognition (evaluated by the Leganés cognitive test), depression (assessed by the Geriatric Depression Scale-15), and in-hospital mobility (evaluated by the Short Physical Performance Battery). The dependent variable functional capacity was assessed by the Katz scale. These instruments were applied at 2 different times: upon admission (within first 24 hours) and at discharge (12-24 hours before). Functional trajectories were defined as the course of functioning from preadmission until discharge using functional capacity data. A multivariate analysis with generalized estimating equation estimated the longitudinal changes in functional capacity. RESULTS AND DISCUSSION: The final sample consisted of 1191 older adults and 53.9% were less than 70 years of age. Regarding changes in functional capacity, 52.5% of the older adults presented worse functional capacity at discharge than at baseline. Being dependent for IADL instrumental daily living activities, the presence of depressive symptoms, low levels of cognition, and in-hospital mobility were risk factors for greater loss in functional capacity during a hospitalization event. CONCLUSION: Hospitalization events may be catastrophic for functional capacity in older adults in Brazil. Functional, cognitive, and emotional status and in-hospital mobility must be carefully assessed at hospital admission and monitored during hospitalization. Effective strategies for preventing loss in functional capacity in older people must improve in the Brazilian hospital system.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".