Comparing the psychological stress between non-smoking patients and smoking patients who experience abrupt smoking cessation during hospitalization for acute myocardial infarction: a pilot study.
Bibliographic record
Abstract
BACKGROUND: Stress is an untoward condition in patients with acute myocardial infarction (AMI). Abrupt nicotine withdrawal is associated with increased symptoms of stress. However, little is known about the impact of smoking cessation on the psychological indicators of stress among hospitalized AMI patients. PURPOSE: In this pilot study we compared the psychological stressors between non-smoking AMI patients and smoking patients who abruptly ceased smoking following admission to the CCU. METHODS: A cross-sectional survey was piloted on a sample of 57 AMI patients (29 smokers and 28 nonsmokers) on the second day of admission to the CCU. Psychological stress was measured using the Profile of Mood States and the Insomnia Severity Index. RESULTS: Multivariate analysis of covariance (MANCOVA) suggested that after adjusting for age, smokers experienced significantly higher overall levels of stress than non-smokers (F = 3.13; p = 0.016). Post-hoc analyses suggested that scores of depression (p = 0.033), anxiety (p = 0.007), and anger (p = 0.017) were particularly higher among smokers, as compared to non-smokers. However, the two groups were not different with regard to their scores on fatigue (p = 0.528) and insomnia (p = 0.299). CONCLUSIONS: Abrupt smoking cessation may expose patients admitted with AMI symptoms to higher levels of psychological stress. Given the potential damaging impact of psychological stressors on the physical outcomes of these patients, these findings demonstrate the need for continued assessment and research related to the management of nicotine withdrawal following AMI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".