Brief Report: Differences in Types of Myocardial Infarctions Among People Aging With HIV
Bibliographic record
Abstract
BACKGROUND: Type 1 myocardial infarctions (T1MIs) result from atherosclerotic plaque instability, rupture, and/or erosion. Type 2 MIs (T2MIs) are secondary to causes such as sepsis and cocaine-induced vasospasm resulting in an oxygen demand-supply mismatch and are associated with higher mortality than T1MIs. T2MIs account for a higher proportion of MIs among people living with HIV (PLWH) compared with the general population. We compared MI rates by type among aging PLWH. We hypothesized that increases in MI rates with older age would differ by MI types, and T2MIs would be more common than T1MIs in younger individuals. METHODS: Potential MIs from 6 sites were centrally adjudicated using physician notes, electrocardiograms, procedure results, and laboratory results. Reviewers categorized MIs by type and identified causes of T2MIs. We calculated T1MI and T2MI incidence rates. Incidence rate ratios were calculated for T2MI vs. T1MI rates per decade of age. RESULTS: We included 462 T1MIs (52%) and 413 T2MIs (48%). T1MI rates increased with older age, although T1MIs occurred in all age decades including young adults. T2MI rates were significantly higher than T1MI rates for PLWH younger than 40 years. T1MI rates were similar or higher than T2MI rates among those older than 40 years (significantly higher for those aged 50-59 and 60-69 years). CONCLUSIONS: Rates of T2MIs were higher than T1MIs until age 40 years among PLWH, differing from the general population, but rates of both were high among older PLWH. Given prognostic differences between MI types, these results highlight the importance of differentiating MI types among PLWH.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".