Trends in ST-elevation myocardial infarction hospitalization among young adults: a binational analysis
Bibliographic record
Abstract
BACKGROUND: ST-segment myocardial infarction (STEMI) is typically associated with increased age, but there is an important group of patients who suffer from STEMI under the age of 50 who are not well characterized in studies. METHODS AND RESULTS: We analysed results from Myocardial Ischemia National Audit Project (MINAP) from the United Kingdom (UK) between 2010 and 2017 and the National Inpatient Sample (NIS) from the United States of America (USA) between 2010 and 2018. After exclusion criteria, there were 32 719 STEMI patients aged ≤50 from MINAP, and 238 952 patients' ≤50 from the NIS. We analysed temporal trends in demographics, management, and mortality. The proportion of females increased, 15.6% (2010-2012) to 17.6% (2016-2017) (UK) and 22.8% (2010-2012) to 23.1% (2016-2018) (USA). The proportion of white patients decreased, from 86.7% (2010) to 79.1% (2017) (UK) and 72.1% (2010) to 67.1% (2017) (USA). Invasive coronary angiography (ICA) rates increased in UK (2010-2012: 89.0%, 2016-2017: 94.3%), while decreased in USA (2010-2012: 88.9%, 2016-2018: 86.2% (USA). After adjusting for baseline characteristics and management strategies, there was no difference in all-cause mortality in the UK in 2016-2017 compared to 2010-2012 (OR:1.21, 95% CI:0.60-2.40), but there was a decrease in the USA in 2016-2018 compared to 2010-2012 (OR: 0.84, 95% CI: 0.79-0.90). CONCLUSION: The demographics of young STEMI patients have temporally changed in the UK and USA, with increased proportions of females and ethnic minorities. There was a significant increase in the frequency of diabetes mellitus over the respective time periods in both countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".