The Etiology and Management of Critical Acute Right Heart Failure
Bibliographic record
Abstract
Right ventricular failure contributes to the morbidity and mortality of acute myocardial function, massive pulmonary embolism, and chronic pulmonary hypertension. Understanding how the normal physiology of the right ventricle (RV) is disrupted is integral to managing patients who present with RV decompensation. Therapeutic advances in mechanical circulatory support, pharmacotherapies to reduce afterload, mechanical and chemical lytic therapies for acute pulmonary embolism have improved outcomes of patients by offloading the RV. In this report we provide an overview of the physiology of the RV, medical management (volume optimization, hemodynamic targets, rhythm management), along with critical care-specific topics (induction with mechanical ventilation, sedation strategies, and mechanical circulatory support) and provide a framework for managing patients who present with leveraging principles of preload, contractility, and afterload. Last, because of the complexity of right ventricular failure management, and the complexity of presentation, we also discuss the role of team-based approach (cardiogenic shock and pulmonary embolism response teams), and highlight its benefits at improving outcomes.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".