Postoperative Right Ventricular Failure in Cardiac Surgery
Bibliographic record
Abstract
Two cases of patients that developed right ventricular failure (RVF) after cardiac valve surgery are presented with a narrative revision of the literature. RVF involves a great challenge due to the severity of this condition; it has a low incidence among non-congenital cardiac surgery patients, is more likely associated with cardiovascular and pulmonary complications related to cardiopulmonary bypass (CPB), and is a cause of acute graft failure and of a higher early mortality in cardiac transplant. The morphologic and hemodynamic characteristics of the right ventricle and some specific factors that breed pulmonary hypertension after cardiac surgery are in favor of the onset of RVF. Due to the possibility of complications after cardiac valve repair or replacement, measures as appropriate hemodynamic monitoring, to manage oxygenation, ventilation, sedation, acid base equilibrium and perfusion goals are a requirement, as well as a normal circulating volume, and the prevention of a disproportionate rise in the afterload, to preserve the free wall of the right ventricle (RV) and the septum's contribution to the right ventricular global function and geometry. If there is no response to these basic measures, the use of advanced therapy with inotropics, intravenous or inhaled pulmonary vasodilation agents is recommended; the use of mechanical ventricular assistance stands as a last resource.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".