Impact of Nurse Practitioners on Guideline-Directed Medical Therapy at Discharge on Patients with Recent Acute Coronary Syndrome after Coronary Artery Bypass Graft Surgery
Bibliographic record
Abstract
Background: Skills and knowledge of acute care nurse practitioners (ACNPs) are important resources within the healthcare team. Few studies have been conducted on their impact in terms of adherence to guideline-directed medical therapy (GDMT) at the time of discharge. Methods: A retrospective cohort study of 160 patients with a diagnosis of recent acute coronary syndrome (ACS) prior to coronary artery bypass graft surgery in Eastern Canada was conducted. Eighty randomly selected patients in each group were compared, one led by the physicians, and the other with the presence of ACNPs within the team. Results: In the physician-led group, adherence to GDMT at discharge was not reached in 47 patients versus six in the group with ACNPs (58.8% vs. 7.5%; χ2 (1) = 45.58; p = 0.0001). Of the 47 non-adherent patients, 29 suffered a nonST segment elevation myocardial infarction. The main reason for non-adherence in both groups was the omission of dual antiplatelet therapy prescription. Mean length of stay in hours was longer in the physician-led group than in the group with ACNPs (148.1 vs. 127; F (1, 158) = 2.053; p = 0.154). At 30 days, returns to the emergency department (9 vs. 16; χ2 (1) = 2.323, p = 0.127) and readmissions (4 vs. 8; χ2 (1) = 1.441, p = 0.230) for cardiac surgery complications were not statistically different between both groups. Conclusion: On a cardiac surgery unit, the ACNP is a valuable addition with respect to adherence to GDMT at discharge in an ACS population post surgical revascularization.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".