Association Between a Ban on Smoking in a Hospital and the In-Hospital Onset of Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Smoking is an important cardiovascular risk. We hypothesized that a ban on smoking in a hospital could decrease the in-hospital onset of acute myocardial infarction (AMI). METHODS: Our hospital provided separate facilities for smokers and nonsmokers from 1981 to 2002. From 2002 to 2006, we began to introduce smoke-free zones throughout the entire building. During this period, smoking areas and smoking tables were abolished, until the entire hospital became a non-smoking area in 2007. We registered patients who experienced an in-hospital onset of AMI from January 2002 to June 2014. Patients with an in-hospital onset of AMI were defined as those who had AMI but were not under the care of the Departments of Cardiology or Emergency. We observed 25 patients (males/females, 16/9; average age, 70 years) with an in-hospital onset of AMI from 2002 to 2014. RESULTS: The incidence of in-hospital AMI significantly decreased as the stages of non-smoking areas progressed (P for trend 0.010). Six of the 25 patients died after AMI. The death group showed significantly higher serum levels of peak creatine kinase and lower levels of hemoglobin. In addition, 10 of the 25 patients developed in-hospital AMI after surgery. Anti-coagulant therapy was canceled before an operation in three patients. After an operation, advanced anemia was seen in four patients. In addition, there were no differences in the patient characteristics between the smoking and non-smoking groups except for dyslipidemia. CONCLUSION: The spread of a non-smoking policy significantly decreased the in-hospital onset of AMI in our hospital, which suggests that not only direct smoking but also passive smoking is important target for reducing in-hospital AMI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".