Bibliographic record
Abstract
Infections have been recognized as significant causes of cardiac diseases for many decades. Various microorganisms have been implicated in the etiology of these diseases involving all classes of microbial agents. All components of the heart structure can be affected by infectious agents, i.e. pericardium, myocardium, endocardium, valves, autonomic nervous system, and some evidence of coronary arteries. A new breed of infections have evolved over the past three decades involving cardiac implants and this group of cardiac infectious complications will likely continue to increase in the future, as more mechanical devices are implanted in the growing ageing population. This article will review the progress made in the past decade on understanding the pathobiology of these infectious complications of the heart, through advances in genomics and proteomics, as well as potential novel approach for therapy.An up-to-date, state-of-the-art review and controversies will be outlined for the following conditions: (i) perimyocarditis; (ii) infective endocarditis; (iii) cardiac device infections; (iv) coronary artery disease and potential role of infections.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".