Screening for neuropsychological impairment in COPD patients undergoing rehabilitation
Bibliographic record
Abstract
INTRODUCTION: Chronic obstructive pulmonary disease (COPD) is a complex multi-component disorder characterized by progressive irreversible respiratory symptoms and extrapulmonary comorbidities, including anxiety-depression and mild cognitive impairment (MCI). However, the prevalence of these impairments is still uncertain, due to non-optimal screening methods. This observational cross-sectional multicentre study aimed to evaluate the prevalence of anxiety-depressive symptoms and MCI in COPD patients, identify the most appropriate cognitive tests to screen MCI, and investigate specific cognitive deficits in these patients and possible predictive factors. MATERIALS AND METHODS: Sixty-five stable COPD inpatients (n = 65, aged 69.9±7.6 years, mainly stage III-IV GOLD) underwent the following assessments: Hospital Anxiety and Depression Scale (HADS), Geriatric Depression Scale (GDS) or Beck Depression Inventory-II (BDI-II), Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and a complete neuropsychological battery (ENB-2) including different cognitive domains (attention, memory, executive functions, and perceptive and praxis abilities). RESULTS: Moderate-severe anxiety was present in 18.5% of patients and depressive symptoms in 30.7%. The prevalence of MCI varied according to the test: 6.2% (MMSE), 18.5% (MoCA) and 50.8% (ENB-2). In ENB-2, patients performed significantly worse compared to Italian normative data on digit span (5.11±0.9 vs. 5.52±1.0, p = 0.0004), trail making test-B (TMT-B) (176.31±99.5 vs. 135.93±58.0, p = 0.004), overlapping pictures (26.03±8.9 vs. 28.75±8.2, p = 0.018) and copy drawing (1.370.6 vs. 1.61±0.5, p = 0.002). At logistic regression analysis, only COPD severity (p = 0.012, odds ratio, OR, 4.4 [95% CI: 1.4-14.0]) and anxiety symptoms (p = 0.026, OR 4.6 [1.2-17.7]) were significant and independent predictors of the deficit in copy drawing, which assesses visuospatial and praxis skills. CONCLUSION: Given the prevalence of neuropsychological impairments in COPD patients, the routine adoption in rehabilitation of screening tools for mood and cognitive function, including digit span, TMT-B and copy drawing, may be useful to detect psychosocial comorbidities and personalize the rehabilitative program.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".