Inhospital NEMS for patients with coexisting physical frailty and cognitive imparment during acute exacerbation of COPD: a randomized controlled trial
Bibliographic record
Abstract
Introduction: Frailty and cognitive impairments are very prevalent in patients hospitalized by an acute exacerbation of COPD (AECOPD). New approaches that avoid the deterioration during hospitalization in these patients are needed. Objective: To evaluate the effect of a NEMS program in patients with physical frailty, cognitive impairment and AECOPD. Methods: In this randomized clinical trial, patients hospitalized by an AECOPD, with a score <26/30 in the Montreal Cognitive Assessment Test (MOCA), and with a score ≥3/5 points in the Modified Fried Criteria were included and randomized into two groups. Patients in the control group received the standard treatment and patients in the experimental group received a daily NEMS program added to the pharmacological treatment. Lower limb strength was evaluated by hand-held dynamometry, Self-Perceived Health Status was evaluated by the EQ-5D questionnaire and physical capacity was evaluated by the 5-times sit to stand test (5STS). Patients were evaluated at baseline and at hospital discharge. Results: Finally, 49 patients were included in this study. There were no differences between groups at baseline in age, BMI, disease severity, hospital stay and main variables. There were significant differences between group at discharge in right lower limb strength and EQ-5D VAS (p<0.001 and p=0.018 respectively). There were not significant differences between groups in 5 STS (p=0.06). Conclusion: This study has evidenced the effectiveness of a NEMS program in patients with coexisting physical frailty and cognitive imparment during acute exacerbation of COPD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".