New developments in the understanding of right ventricular function in acute care
Bibliographic record
Abstract
PURPOSE OF REVIEW: Right ventricular dysfunction has an important impact on the perioperative course of cardiac surgery patients. Recent advances in the detection and monitoring of perioperative right ventricular dysfunction will be reviewed here. RECENT FINDINGS: The incidence of right ventricular dysfunction in cardiac surgery has been associated with unfavorable outcomes. New evidence supports the use of a pulmonary artery catheter in cardiogenic shock. The possibility to directly measure right ventricular pressure by transducing the pacing port has expanded its use to track changes in right ventricular function and to detect right ventricular outflow tract obstruction. The potential role of myocardial deformation imaging has been raised to detect patients at risk of postoperative complications. SUMMARY: Perioperative right ventricular function monitoring is based on echocardiographic and extra-cardiac flow evaluation. In addition to imaging modalities, hemodynamic evaluation using various types of pulmonary artery catheters can be achieved to track changes rapidly and quantitatively in right ventricular function perioperatively. These monitoring techniques can be applied during and after surgery to increase the detection rate of right ventricular dysfunction. All this to improve the treatment of patients presenting early signs of right ventricular dysfunction before systemic organ dysfunction ensue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".