Outcomes of nurse practitioner‐led care in patients with cardiovascular disease: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: To assess randomized controlled trials evaluating the impact of nurse practitioner-led cardiovascular care. BACKGROUND: Systematic review of nurse practitioner-led care in patients with cardiovascular disease has not been completed. DESIGN: Systematic review and meta-analysis. DATA SOURCES: The Cochrane Central Register of Controlled Trials (CENTRAL), Medline, Embase, CINAHL, Web of Science, Scopus and ProQuest were systematically searched for studies published between January 2007 - June 2017. REVIEW METHODS: Cochrane methodology was used for risk of bias, data extraction and meta-analysis. The quality of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation approach. RESULTS: Out of 605 articles, five articles met the inclusion criteria. There was no statistical difference between nurse practitioner-led care and usual care for 30-day readmissions, health-related quality of life and length of stay. A 12% reduction in Framingham risk score was identified. CONCLUSION: There are a few randomized control trials assessing nurse practitioner-led cardiovascular care. IMPACT: Low to moderate quality evidence was identified with no statistically significant associated outcomes of care. Nurse practitioner roles need to be supported to conduct and publish high-quality research.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".