Pain-Friendly Strategies: Nursing Intervention in Postoperative Myocardial Revascularization
Bibliographic record
Abstract
Introduction. Pain is the main symptom in the outpatient postoperative period after myocardial revascularization, negatively impacting on physical mobility, rest, emotional well-being and patient recovery. Objective. To determine the effect of a nursing educational intervention on pain reduction in patients undergoing myocardial revascularization during the outpatient postoperative period. Method. A quantitative, quasi-experimental study, with pretest-posttest design. Eighty revascularized patients randomly assigned to an experimental group and a comparison group at a private institution in Cúcuta (Colombia) participated. The McGill Pain Questionnaire and the Inventory of Distress-State Anxiety (IDARE) were used to evaluate the effect of the intervention, after the participants signed the informed consent form. Results. The nursing educational intervention significantly reduced pain and anxiety in the experimental group, both overall and in each of its dimensions (p < 0.05). In the comparison group, no statistically significant changes were observed (p > 0.05). The educational intervention with structured follow-up favored the comprehensive recovery of patients who had undergone myocardial revascularization in the home environment. Conclusion. The nursing educational intervention was effective in reducing postoperative pain and associated symptoms in patients with myocardial 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".