A systematic review examining the clinical and health-care outcomes for congenital heart disease patients using home monitoring programmes
Bibliographic record
Abstract
Objectives This review aimed to present the clinical and health-care outcomes for patients with congenital heart disease (CHD) who use home monitoring technologies. Methods Five databases were systematically searched from inception to November 2020 for quantitative studies in this area. Data were extracted using a pre-formatted data-collection table which included information on participants, interventions, outcome measures and results. Risk of bias was determined using the Cochrane Risk of Bias 2 tool for randomised controlled trials (RCTs), the Newcastle–Ottawa Quality Assessment Scale for cohort studies and the Institute of Health Economics quality appraisal checklist for case-series studies. Data synthesis: Twenty-two studies were included in this systematic review, which included four RCTs, 12 cohort studies and six case-series studies. Seventeen studies reported on mortality rates, with 59% reporting that home monitoring programmes were associated with either a significant reduction or trend for lower mortality and 12% reporting that mortality trended higher. Fourteen studies reported on unplanned readmissions/health-care resource use, with 29% of studies reporting that this outcome was significantly decreased or trended lower with home monitoring and 21% reported an increase. Impact on treatment was reported in 15 studies, with 67% of studies finding that either treatment was undertaken significantly earlier or significantly more interventions were undertaken in the home monitoring groups. Conclusion The use of home monitoring programmes may be beneficial in reducing mortality, enabling earlier and more timely detection and treatment of CHD complication. However, currently, this evidence is limited due to weakness in study designs.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".