Differences in cognitive functioning between patients with chronic cardiopulmonary disease
Bibliographic record
Abstract
Introduction: Patients with lung disease may present cognitive impairment, mainly associated with hypoxemia, affecting functions such as memory and decision-making Objective: To analyze the differences between cognitive impairment in patients with COVID-19, Interstitial Lung Disease (ILD), Heart Failure (HF) and Chronic Obstructive Pulmonary Disease (COPD) Methods: 304 patients participated in a cross-sectional study, divided according to their diagnosis into: COVID-19 (G1, n=83, 53.01% women, 57.6±14.9 years), ILD (G2, n=108, 48.14% women, 61.5±11.8 years), HF (G3, n=73, 50.68%, 62±12.9 years) and COPD (G4, n=40, 66.66% women, 70.8±10.7 years). To assess cognitive impairment, the Montreal Cognitive Assessment (MoCA) used, all dimensions taken into account and patients divided according to the level of impairment (without impairment, mild and severe). A Kruskal-Wallis analysis and contingency tables performed in Jamovi 2.3 Results: Significant differences (p<0.001) found in the level of cognitive impairment (G1/G2/G3/G4): no impairment (34.9%/9.3%/13.7%/15%), mild impairment (59%/81.5%/78.1%/65%), severe impairment (6%/9.3%/8.2%/20%). Statistically significant differences (p>0.05) were found in the dimensions evaluated: memory (G1/G2= -4.28, G1/G3= -3.81), executive function (G1/G2= -4.24, G1/G4= -3.93), attention (G1/G2= -4.72, G2/G3=3.58), language (G1/G2= -6.83, G1/G4= -3.78, G2/G3= 4.85) and orientation (G1/G4= -3.85, G2/G4= -4.23, G3/G4= -4.15, G3/G4= -4.15) Conclusions: Patients with chronic diseases may suffer cognitive impairment; however, the effects will depend on the pathology. It is essential to develop cognitive rehabilitation strategies to improve the quality of life of patients
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".