Functional status and life satisfaction of patients with stable angina pectoris in Austria
Bibliographic record
Abstract
OBJECTIVES: Although substantial progress in the treatment of stable angina pectoris (sAP) has been made, little is known about the functional status and quality of life (QoL) of patients in different healthcare systems. DESIGN AND METHODS: We undertook a survey using the Seattle Angina Questionnaire (SAQ) (five domains scored form 0-worst assessment to 100-best assessment) to assess symptoms, QoL (including limitation of activities), demographics, geographic distribution and individual disease data in patients with stable coronary artery disease in Austrian cardiology practices. RESULTS: A total of 660 patients with sAP with a mean age of 69.2 years were included. SAQ scores were 67.5±24.4 for physical limitation, 65.5±26.6 for angina stability, 79.3±23.2 for angina frequency, 86.3±16.2 for treatment satisfaction and 63.7±24.2 for overall QoL. Multiple regression identified male gender, but also female gender, Eastern Austrian residence and high body mass index as predictive factors for SAQ scoring. A total of 35.6% of the patients reported at least one desirable activity that was limited through AP symptoms. CONCLUSIONS: Activity and QoL assessments are in accordance with published literature: The number and the diversity of desired activities indicate the need to focus on patient's individual activity level to improve symptom management.
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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 | 0.000 |
| 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) | 0.002 | 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".