FactorsAssociated with Patient Symptoms in Ischemic Heart Patients Awaiting Coronary Artery Bypass Grafting
Bibliographic record
Abstract
Objective: To investigate the correlation between the left ventricular ejection fraction (LVEF), waiting time, comorbidity, depression and patient symptoms in ischemic heart patients awaiting coronary artery bypass grafting (CABG). Methods: A cross-sectional correlational design. Results: The 88 eligible inpatients, (61 men, 27 women) with an average age of 67.9 years (S.D.= 10.1). The average LVEF was 52.6% (S.D. = 16.4). The average waiting time was 34.6 days(S.D. = 35.9). The most frequently reported co-morbid disease was hypertension. Depression score was 9.1 points on average (S.D. = 5.6), and the most common symptoms while awaiting CABG included shortness of breath and chest pain. Factors significantly associated with patient symptoms were comorbidity, waiting time, and depression(r = .256; p <.05, r = .283; p <.01, r = .476; p<.01), but no significant association was found between LVEF and patient symptoms (r = -.031; p = .775). Conclusion: The study findings could be used to develop interventions or guidelines to provide nursing care to manage patient symptoms while awaiting CABG. The foreseeable guidelines should consider including LVEF, waiting time, comorbidities and depression. Keywords: Ischemic heart patients, patient symptoms, awaiting coronary artery bypass grafting
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".