Heart failure clinics and outpatient management: review of the evidence and call for quality assurance
Bibliographic record
Abstract
Despite major advances in treatment options for heart failure patients, morbidity and mortality remain unacceptably high. Frequent readmissions are distressful for patients and are associated with large costs for society. In an attempt to improve care for heart failure patients and thereby reduce morbidity and hospital readmissions, specialised heart failure clinics have emerged over the last 10 years. In particular, clinics relying, at least in part, on nurses specially trained in heart failure have gained popularity. This review of the published literature describes the wide variety of designs and the types of interventions taking place in such heart failure clinics. A total of 18 randomised studies comparing heart failure clinics using nurse intervention with conventional care have been published to date, and the majority of these have shown either a reduction in hospital readmissions or shortening of hospitalisations in the intervention group. These findings are supported by the results of several non-randomised, controlled investigations. Thus, it is concluded that heart failure clinics using nurse intervention should be an integrated part of the care process for patients with heart failure wherever possible. We argue that ongoing attention should be paid to the quality of care delivered by the clinics to ensure that the benefit of this intervention strategy persists. Thus, it would be of importance to continuously record relevant data describing the care process using specific indicators such as ACE-inhibitor and beta-blocker use and doses. One possible, practical method to apply such continuous quality assurance may be by means of electronic medical record databases.
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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 | 0.022 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".