Memory loss following coronary artery bypass graft surgery: A discussion of the implications for nursing.
Bibliographic record
Abstract
AIM: The aim of this discursive paper is to present nursing interventions that address memory loss following heart surgery and that can be incorporated into patients' overall plan of care. BACKGROUND: Coronary artery bypass graft (CABG) is the most frequent surgical treatment for cardiovascular disease. Despite the advantages, reports indicate CABG procedures significantly increase the risk of cerebral impairment and/or injury, which can present itself in the form of memory loss. Older individuals tend to be at higher risk for memory loss than other age groups. Age combined with the effects of the surgical procedure increases the likelihood that individuals over the age of 65 years will experience some form of memory loss following surgery. In this paper, the author presents a discussion of the relevance and implications of memory loss to clinical nursing practice with particular attention to strategies nurses should use when caring for patients experiencing this symptom. RELEVANCE TO CLINICAL PRACTICE: Memory loss is a common symptom present in at least 25% of all patients following CABG. Screening for memory loss following CABG using reliable and valid instruments, revisions to current patient education initiatives to include calling patients following hospital discharge to review education and delivering education over multiple sessions, creating supportive reality-oriented relationships, and engaging in memory oriented training are suggested as nursing strategies that should be incorporated into existing nursing care for patients following CABG. CONCLUSION: Currently, nursing practice does not routinely incorporate assessment and management of memory loss into the overall plan of care for patients following heart surgery. Specific nursing strategies that centre on the assessment and management of memory loss need to be implemented into the standard of nursing practice.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".