Exploring outcomes of a nurse practitioner‐managed cardiac surgery follow‐up intervention: a randomized trial
Bibliographic record
Abstract
AIMS: To describe and compare the outcomes of a nurse practitioner-managed cardiac surgery follow-up model of care with the standard model of primary care provider follow-up for coronary artery bypass graft surgery patients. BACKGROUND: Advances in healthcare have had a favourable impact on length of stay following cardiac surgery; however, the shorter length of stay has not been accompanied by enhanced support to bridge the gap between acute care and the community setting. DESIGN: Prospective (2009-2010) randomized study. METHODS: Elective cardiac surgery patients (N = 200) were randomly assigned to the nurse practitioner follow-up intervention or to the standard model of follow-up care. The main outcomes were health-related quality of life, patient satisfaction, symptoms, and health resource use. Outcome data were elicited via telephone interviews at 2 and 6 weeks postdischarge. RESULTS: Baseline differences between the two groups were non-significant; however, at 2 weeks postdischarge, the intervention group reported significantly fewer symptoms and higher physical functioning status. At 2 and 6 weeks postdischarge, the intervention group was significantly more satisfied with the amount of help, as well as the quality of the services received. Differences in healthcare resource use were not statistically significant. CONCLUSION: This evidence suggests that the nurse practitioner-managed model of follow-up care effectively bridges the gap between institutional and primary care in the cardiac surgery population.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".