Nurse practitioners in postoperative cardiac surgery: are they effective?
Bibliographic record
Abstract
BACKGROUND: High demand for acute care nurse practitioners (ACNPs) in Canadian postoperative cardiac surgery settings has outpaced methodologically rigorous research to support the role. PURPOSE: To compare the effectiveness of ACNP-led care to hospitalist-led care in a postoperative cardiac surgery unit in a Canadian, university-affiliated, tertiary care hospital. METHODS: Patients scheduled for urgent or elective coronary artery bypass and/or valvular surgery were randomly assigned to either ACNP-led (n=22) or hospitalist-led (n=81) postoperative care. Both ACNPs and hospitalists worked in collaboration with a cardiac surgeon. Outcome variables included length of hospital stay, hospital readmission rate, postoperative complications, adherence to follow-up appointments, attendance at cardiac rehabilitation and both patient and health care team satisfaction. RESULTS: Baseline demographic characteristics were similar between groups except more patients in the ACNP-led group had had surgery on an urgent basis (p < or = 0.01), and had undergone more complicated surgical procedures (p < or =0.01). After discharge, more patients in the hospitalist-led group had visited their family doctor within a week (p < or =0.02) and measures of satisfaction relating to teaching, answering questions, listening and pain management were higher in the ACNP-led group. CONCLUSION/IMPLICATIONS: Although challenges in recruitment yielded a lower than anticipated sample size, this study contributes to our knowledge of the ACNP role in postoperative cardiac surgery. Our findings provide support for the ACNP role in this setting as patients who received care from an ACNP had similar outcomes to hospitalist-led care and reported greater satisfaction in some measures of care.
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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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".