Relationship between health-related quality of life and cognitive function in patientswith cardiorespiratory failure
Bibliographic record
Abstract
Background: Cardiorespiratory failure (CF) patients can presented cognitive impairment (CI) due to processes such as hypoxemia, hypoxia, cerebrovascular disease and peripheral arterial disease. CI can negatively affect the functionality and health-related quality of life (HRQoL) of patients. Purpose: To investigate the differences in quality of life in CF patients, according to their cognitive functioning. Methods: A cross-sectional was conducted, 98 CF patients participated. SF-12 health questionnaire was used to measure HRQoL. Montreal Cognitive Assessment (MoCA) was used for evaluate cognitive function, ≤ 26 points indicate CI. The patients were classified into two groups, G1: no CI (n=15, 63.20±14.61 years, 73.3% men) and G2: CI (n=83, 68.22±14.38 years, 60.7% woman). Student9s t-test in SPSS v25was used. Results: Significant statistically differences were found (p<0.05) between both groups (G1vsG2) in HRQoL (64.11±27.30 vs 50.70±24.55). Specifically: body pain (89.29±27.23 vs 64.34±37.48), general health (55.36±26.27 vs 38.60±26.07) and social function (80.77±25.31 vs 38.60±26.07). Physical function (55.36±41.80 vs 42.64±36.91), physical role (53.57±49.86 vs 35.29±43.24), vitality (68.57±32.07 vs 49.41±34.97), emotional role (60.71 ±44.62 vs 52.94±42.21) and mental health (69.04±25.43 vs60.14±26.86) only presented clinically significant differences. Conclusions: These patients showed differences in HRQoL, regarding the presence of CI. These alterations have a significant impact on mental and physical health, daily life and functionality of patients. So it is necessary to address and investigate more about this problem in this poorly studied population
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".