EXAMINING THE IMPACT OF UPRIGHT TIME ON FRAILTY CHANGES IN ACUTE CARE
Bibliographic record
Abstract
The objective of this study was to determine the association between duration and frequency of upright time with frailty changes in acute care. One-hundred and thirty patients were recruited within 48-hr of admission to acute care. Frailty was measured with a 30-item frailty index at admission and at 2 weeks or at hospital discharge in those with a hospital length of stay less than 2 weeks. The frequency and duration of bouts of upright time were measured daily using ActivPAL inclinometers. Ninety-two participants (mean age 82.1 ± 8.0; 57.7% female) had complete ActivPAL and frailty data. Median upright time/day during awake hours (6AM-10PM) was 44.7 (IQR: 24.9–63.0) minutes. The frequency of upright bouts/day during awake hours was 19.2 (IQR: 9.5–30) and the median upright bout duration was 2.8 (IQR: 1.8–4.2) minutes. The frequency of upright bouts/day was independently associated with lower frailty levels at hospital discharge/2weeks (β-coefficient: -0.0022, 95% CI: -0.0001 to -0.004; p=0.04) after adjusting for age, sex, and frailty at admission; whereas, upright bout duration was not. Seventy patients (76%) accumulated daily ≥5 bouts/day lasting at least 2 minutes. Those patients had significantly lower frailty scores at 2 weeks/discharge (β-coefficient: -0.086, 95% CI: -0.014 to -0.157; p=0.01) and a shorter hospital length of stay (12 days; 95% CI: 0.270–24.11 days; p=0.05) compared to those who did not meet these criteria. Reducing bed rest with upright time in acute care is linked to lower frailty levels at hospital discharge. A clinical trial is needed to confirm these findings.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".