In a prospective population-based study, the degree of mobility impairment during hospitalisation is associated with higher degrees of frailty
Bibliographic record
Abstract
BACKGROUND: Hospitals pose a high risk for frailty to develop or accelerate. Still, few community-based cohort studies follow patients before, during, and after hospitalisation. We investigated the degree of immobility during hospitalisation and its impact on subsequent frailty. METHODS: In a prospective population-based cohort of individuals aged ≥ 70 from a London UK borough, we performed comprehensive community assessments at baseline and after two years. At each hospitalisation, we measured daily mobility and other clinical variables. Acute immobility burden, a summative level of poor mobility for all hospitalisations, was calculated for each participant and operationalized as low/high based on the population median. A frailty index was calculated for all participants during baseline and follow-up assessments. We estimated the effect of these exposures on follow-up frailty index scores using linear regression. RESULTS: We included 1177 participants. Those admitted (N = 114) were assessed over 1999 bed-days. The degree of baseline frailty had the largest association with subsequent frailty. However, a high immobility burden during hospitalisation was consistently related to additional increases in frailty (low burden: β = 0.02 per unit increase in FI (95%CI: -0.002-0.04), high burden: β = 0.07, (95%CI: 0.041-0.10)). Immobility burden remained associated with subsequent frailty even when limiting the analysis to: those who were independently mobile; the first seven days of hospitalisation; and accounting for illness severity. High immobility burden was prognostic of subsequent death. CONCLUSIONS: The degree of immobility during hospitalisation, a potentially modifiable risk factor, may determine whether hospitalisation contributes to increasing frailty.
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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 | 0.004 |
| 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 | 0.001 |
| 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".