THE EFFECT OF TELEPHONE SUPPORT INTERVENTIONS ON CORONARY ARTERY DISEASE (CAD) PATIENT OUTCOMES DURING CARDIAC REHABILITATION: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Background Cardiac rehabilitation is offered to individuals after cardiac events to aid recovery and reduce the likelihood of further cardiac illness. However, patient participation remains suboptimal and the provision of high quality care to an expanding population of patients with chronic heart conditions is becoming increasingly difficult. As a result, the feasibility and effectiveness of using telehealth interventions to deliver care have recently been considered. Objectives To determine the effect of telephone support interventions compared with standard post-discharge care on coronary artery disease patient outcomes. Methods We searched The Cochrane Library, MEDLINE, EMBASE, and CINAHL. Reference lists of included studies were also checked. No language restrictions were applied. Selection criteria: We included randomized controlled trials that directly compared telephone interventions with standard post-discharge care in adults following a myocardial infarction, angina or a revascularization procedure. Data collection and analysis Studies were selected independently by two reviewers. Data were extracted by a single reviewer and checked by a second one. Where appropriate, outcome data were pooled and analyzed using a random effects model. For dichotomous variables, odds ratios (OR) and 95% confidence intervals (CI) were derived for each outcome. For continuous variables, standardized mean differences (SMD) and 95% CI were calculated for each outcome. Results Thirty-two studies met the inclusion criteria. No difference was observed in mortality between the telephone group and the group receiving standard care (OR 1.02 (0.69, 1.62)). The intervention was however significantly associated with fewer hospitalizations than the comparison group (OR 0.62 (0.40, 0.97)). Significantly more participants in the telephone group stopped smoking (OR 1.40 (1.08, 1.82)); had lower LDL levels (SMD −0.19 (−0.39, −0.00)); lower SBP (SMD −0.22 (−0.36, −0.07)); and higher physical composite scores for quality of life (SMD 0.15 (0.01, 0.30)). However, no significant differences were observed for medication adherence (OR 0.78 (0.78, 1.28)); and the mental composite score for quality of life (SMD −0.00 (−0.19, 0.18)). Conclusions Regular telephone support interventions may help increase the uptake of secondary prevention and reduce further hospitalization.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".