The challenges in the management of right ventricular infarction
Bibliographic record
Abstract
In recent years, right ventricular (RV) infarction seems to be underdiagnosed in most cases of acute myocardial ischaemia despite its frequent association with inferior-wall and, occasionally, anterior-wall myocardial infarction (MI). However, its initial management is drastically different from that of left ventricular MI, and studies have indicated that RV infarction remains associated with significant morbidity and mortality, even in the mechanical reperfusion era. The pathophysiology of RV infarction involves the interaction between the right and left ventricle (LV), and the mechanism has been clarified with the advent of diagnostic non-invasive modalities, such as echocardiography and cardiac magnetic resonance. In recent years, considerable progress has been made in the treatment of RV infarction; early revascularization remains the cornerstone of the management, and fluid resuscitation, with appropriate target selection, is necessary to maintain appropriate preload. Early recognition in intensive care with clear understanding of the pathophysiology is essential to improve its prognosis. In terms of management, the support strategy for RV dysfunction is different from that for LV dysfunction since the former may often be temporary. Along with early reperfusion, maintenance of an adequate heart rate and atrioventricular synchrony are essential to sustain a sufficient cardiac output in patients with RV infarction. In refractory cases, more intensive mechanical support is required, and new therapeutic options, such as Tandem-Heart or percutaneous cardiopulmonary support systems, are being developed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".