<i>Editorial Commentary</i>: Myocardial Infarction in HIV-Infected Persons: Time to Focus on the Silent Elephant in the Room?
Bibliographic record
Abstract
(See the HIV/AIDS Major Article by Rasmussen et al on pages 1415–23.) The landscape of human immunodeficiency virus (HIV) care shifted dramatically in 1996 with the arrival of new drugs and antiretroviral combinations. Many patients, while living their lives without the risk of AIDS-defining opportunistic infections or cancers, are now starting to experience medical conditions commonly associated with aging. The focus of much of their medical care has now shifted toward comorbidity management and treating the common conditions associated with aging. One area that has attracted intense attention is the high incidence of cardiovascular disease (CVD) being seen in individuals with HIV. HIV cohort studies now are consistently reporting an increased risk for experiencing a myocardial infarction (MI) of 1.5- to 2-fold [1]. Recent articles studying the causes of death in developed-country HIV cohorts have reinforced this concern by reporting that about 10% of deaths in their HIV patients are due to CVD and MI [2, 3]. The pathophysiology of the underlying processes explaining this figure is hotly debated and widely discussed [4]. There is evidence to suggest that HIV infection directly causes both chronic inflammation and lipid disturbances, which may act as an accelerant for incident CVD [4, 5]. A role for individual antiretroviral agents as well as classes of agents, either directly or indirectly through lipid perturbation or inflammation, has also been proposed as a contributing factor [6]. An unfavorable genetic background (in the context of traditional CVD risk factors) and poorly controlled hypertension have also both recently been proposed as contributors to the increased incidence of MI in HIV-infected populations [7, 8]. Intense basic science research and pharmaceutical company attention have been spent on explaining and offering strategies to clinicians for mitigating these risks. One confounding factor in many of the cohort observational studies is the well-known very high rate of tobacco smoking in the HIV-infected population [9]. Due to smoking's strong association with CVD, it has been the proverbial “silent elephant in the room” in any observational study exploring CVD in HIV infection.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.034 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".