Intensive Care Unit Management of Right Heart Failure and LungTransplantation for Pulmonary Hypertension
Bibliographic record
Abstract
: Pulmonary hypertension is associated with worse outcomes across systemic and cardiopulmonary conditions. Right ventricular (RV) dysfunction often leads to poor outcomes due to a progressive increase in RV afterload. Recognition and management of RV dysfunction are important to circumvent hospitalization and improve patient outcomes. Early recognition of patients at risk for RV failure is important to ensure that medical therapy is optimized and, where appropriate, referral for lung transplant assessment is undertaken. Patients initiated on parenteral prostanoids and those with persistent intermediate to high risk for poor outcomes should be referred. For patients with RV failure, identifying reversible causes should be a priority in conjunction with efforts to optimize RV preload and strategies to reduce RV afterload. Admission to a monitored environment where vasoactive medications can treat RV failure and its sequelae, such as renal dysfunction, is essential in patients with severe RV failure. Exit strategies need to be identified early on, with consideration and implementation of extracorporeal support for those in whom recovery or transplantation are viable options. Enlisting the skills and support of a palliative care team may improve the quality of life for patients with limited options and those with ongoing symptoms from heart failure in the face of medical treatments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".