Cognitive status among patients with chronic obstructive pulmonary disease
Bibliographic record
Abstract
PURPOSE: We investigated the association between cognitive impairment and chronic obstructive pulmonary disease (COPD), taking into account demographic and clinical variables evaluated during routine practice. PATIENTS AND METHODS: We performed a post hoc analysis of a cross-sectional study that included subjects with stable COPD. Sociodemographic and clinical information was recorded using the Body mass index, airflow Obstruction, Dyspnea and Exacerbations index and the Charlson comorbidity index. Cognitive performance was studied by the mini-mental state examination, with a score less than 27 indicating clinical impairment. Depressive symptoms, physical activity, and quality of life (EuroQoL-5 dimensions and COPD Assessment Test) were also evaluated. RESULTS: The analysis included 940 subjects. The prevalence of cognitive impairment was 39.4%. Multivariate logistic regression models revealed that cognitive impairment was associated with educational level (odds ratio [OR] =0.096, 95% confidence interval [CI] =0.011-0.447) and poorer quality of life measured by the EuroQoL-5 dimensions social tariff (OR =0.967, 95% CI =0.950-0.983). When questionnaires were not included in the analysis, cognitive impairment was associated with educational level (OR =0.063, 95% CI =0.010-0.934), number of exacerbations (OR =11.070, 95% CI =1.450-84.534), Body mass index, airflow Obstruction, Dyspnea and Exacerbations index score (OR =1.261, 95% CI =1.049-1.515), and the Charlson comorbidity index (OR =1.412, 95% CI =1.118-1.783). CONCLUSION: Cognitive impairment is common in COPD and is associated with low educational level, higher disease severity, and increased comorbidity. This could have therapeutic implications for this population.
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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 | 0.003 |
| 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 | 0.000 |
| 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".