Factors associated with actively working in the very long-term following acute coronary syndrome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Returning to work after an episode of acute coronary syndrome (ACS) is challenging for many patients, and has both personal and social impacts. There are limited data regarding the working status in the very long-term after ACS. METHODS: We retrospectively analyzed 1,632 patients who were working prior to hospitalization for ACS in a quaternary hospital and were followed-up for up to 17 years. Adjusted models were developed to analyze the variables independently associated with actively working at the last contact, and a prognostic predictive index for not working at follow-up was developed. RESULTS: The following variables were significantly and independently associated with actively working at the last contact: age>median (hazard-ratio [HR], 0.76, p<0.001); male sex (HR, 1.52, p<0.001); government health insurance (HR, 1.36, p<0.001); history of angina (HR, 0.69, p<0.001) or myocardial infarction (MI) (HR, 0.76, p=0.005); smoking (HR, 0.81, p=0.015); ST-elevation MI (HR, 0.81, p=0.021); anterior-wall MI (HR, 0.75, p=0.001); non-primary percutaneous coronary intervention (PCI) (HR, 0.77, p=0.002); fibrinolysis (HR, 0.61, p<0.001); cardiogenic shock (HR, 0.60, p=0.023); statin (HR, 3.01, p<0.001), beta-blocker (HR, 1.26, p=0.020), angiotensin-converting enzyme (ACE) inhibitor/angiotensin II receptor blocker (ARB) (HR, 1.37, p=0.001) at hospital discharge; and MI at follow-up (HR, 0.72, p=0.001). The probability of not working at the last contact ranged from 24.2% for patients with no variables, up to 80% for patients with six or more variables. CONCLUSIONS: In patients discharged after ACS, prior and in-hospital clinical variables, as well as the quality of care at discharge, have a great impact on the long-term probability of actively working.
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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 | 0.001 | 0.000 |
| 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 it