The clinical effectiveness and cost‐effectiveness of clinical nurse specialist‐led hospital to home transitional care: a systematic review
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Clinical nurse specialists (CNSs) are major providers of transitional care. This paper describes a systematic review of randomized controlled trials (RCTs) evaluating the clinical effectiveness and cost-effectiveness of CNS transitional care. METHODS: We searched 10 electronic databases, 1980 to July 2013, and hand-searched reference lists and key journals for RCTs that evaluated health system outcomes of CNS transitional care. Study quality was assessed using the Cochrane Risk of Bias and Quality of Health Economic Studies tools. The quality of evidence for individual outcomes was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool. We pooled data for similar outcomes. RESULTS: Thirteen RCTs of CNS transitional care were identified (n = 2463 participants). The studies had low (n = 3), moderate (n = 8) and high (n = 2) risk of bias and weak economic analyses. Post-cancer surgery, CNS care was superior in reducing patient mortality. For patients with heart failure, CNS care delayed time to and reduced death or re-hospitalization, improved treatment adherence and patient satisfaction, and reduced costs and length of re-hospitalization stay. For elderly patients and caregivers, CNS care improved caregiver depression and reduced re-hospitalization, re-hospitalization length of stay and costs. For high-risk pregnant women and very low birthweight infants, CNS care improved infant immunization rates and maternal satisfaction with care and reduced maternal and infant length of hospital stay and costs. CONCLUSIONS: There is low-quality evidence that CNS transitional care improves patient health outcomes, delays re-hospitalization and reduces hospital length of stay, re-hospitalization rates and costs. Further research incorporating robust economic evaluation is needed.
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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 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".