Activity performance problems of patients with cardiac diseases and their impact on quality of life
Bibliographic record
Abstract
[Purpose] To describe the functional consequences of patients with cardiac diseases and analyze associations between activity limitations and quality of life. [Subjects and Methods] Seventy subjects (mean age: 60.1±12.0 years) were being treated by Physical Medicine and Rehabilitation and Cardiology Departments were included in the study. Activity limitations and participation restrictions as perceived by the individual were measured by the Canadian Occupational Performance Measure (COPM). The Nottingham Extended Activities of Daily Living (NEADL) Scale was used to describe limitations in daily living activities. To detect the impact of activity limitations on quality of life the Nottingham Health Profile (NHP) was used. [Results] The subjects described 46 different types of problematic activities. The five most identified problems were walking (45.7%), climbing up the stairs (41.4%), bathing (30%), dressing (28.6%) and outings (27.1%). The associations between COPM performance score with all subgroups of NEADL and NHP; total, energy, physical abilities subgroups, were statistically significant. [Conclusion] Our results showed that patients with cardiac diseases reported problems with a wide range of activities, and that also quality of life may be affected by activities of daily living. COPM can be provided as a patient-focused outcome measure, and it may be a useful tool for identifying those problems.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".