Relationship between upper and lower limb function, cognitive impairment, and depression in patients with chronic obstructive pulmonary disease: A cross-sectional study
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) often coexists with limb dysfunction, cognitive decline, and depression. The 20-item Upper Extremity Functional Index (UEFI) and Lower Extremity Functional Index (LEFI) (each 0-80, higher = better) are COPD-specific, yet their links to cognition and mood remain untested. We assessed whether UEFI and LEFI independently predict cognitive impairment and clinically significant depressive symptoms. In this single-center cross-sectional study (January 2021-January 2023), 120 stable out-patients aged ≥40 years with spirometry-confirmed COPD and without confounding neuromuscular disorders were enrolled. Upper and lower limb functions were assessed using the UEFI and LEFI, respectively. Cognitive impairment was defined as a Montreal Cognitive Assessment score < 26; moderate-to-severe depression as a Patient Health Questionnaire-9 score ≥ 10. Both instruments are validated in COPD. Multivariable logistic regression adjusted for age, sex, body mass index, smoking status, forced expiratory volume in 1 second/forced vital capacity ratio, and global initiative for chronic obstructive lung disease stage. Participants (mean age 65.2 ± 7.8 years; 58.3% male) included 35.8% with depression and 29.2% with cognitive impairment. Each 1-standard deviation increase in UEFI reduced the odds of cognitive impairment (adjusted odds ratio: 0.46; 95% confidence interval [CI] 0.31-0.70) and depression (odds ratio: 0.48; 95% CI: 0.30-0.76); corresponding LEFI estimates were 0.65 (95% CI: 0.47-0.90) and 0.51 (95% CI: 0.35-0.74). Associations persisted across sensitivity analyses. Poor self-reported limb function independently predicts cognitive decline and depression in COPD. Incorporating UEFI/LEFI into routine assessment may enable early detection and guide integrated rehabilitation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".