Impact of self-management interventions on stable angina symptoms and health-related quality of life: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Chronic stable angina (CSA) has a major negative impact on health-related quality of life (HRQL) including poor general health status, psychological distress, and inability to self-manage. METHODS: We used meta-analysis to assess the effectiveness of self-management interventions for improving stable angina symptoms, HRQL and psychological well-being. Nine trials, involving 1,282 participants in total, were included. We used standard inverse-variance random-effects meta-analysis to combine the trials. Heterogeneity between trials was evaluated using chi-square tests for the tau-squared statistic and quantified using the I2 statistic. RESULTS: There was significant improvement in the frequency of angina symptoms (Seattle Angina Questionnaire [SAQ], symptom diary) across trials, standardized mean difference (SMD): 0.30 (95% Confidence interval [CI] 0.14, 0.47), as well as reduction in the use of sublingual (SL) nitrates, SMD: -0.49 (95% CI -0.77, -0.20). Significant improvements for physical limitation (SAQ), SMD: 0.38 (95% CI 0.20, 0.55) and depression scores (Hospital Anxiety and Depression Scale), SMD: -1.38 (95% CI -2.46, -0.30) were also found. The impact of SM on anxiety was uncertain due to statistical heterogeneity across trials for this outcome, I2 = 98%. SM did not improve other HRQL dimensions including angina stability, disease perception, and treatment satisfaction. CONCLUSIONS: SM interventions significantly improve angina frequency and physical limitation; they also decrease the use of SL nitrates and improve depression in some cases. Further work is needed to make definitive conclusions about the impact of SM on cardiac-specific anxiety.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.047 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".