Perioperative right ventricular dysfunction
Bibliographic record
Abstract
PURPOSE OF REVIEW: To evaluate new information on the importance of right ventricular function, diagnosis and management in cardiac surgical patients. RECENT FINDINGS: There is growing evidence that right ventricular function is a key determinant in survival in cardiac surgery, particularly in patients with pulmonary hypertension. The diagnosis of this condition is helped by the use of specific hemodynamic parameters and echocardiography. In that regard, international consensus guidelines on the echocardiographic assessment of right ventricular function have been recently published. New monitoring modalities in cardiac surgery such as regional near-infrared spectroscopy can also assist management. Management of right ventricular failure will be influenced by the presence or absence of myocardial ischemia and left ventricular dysfunction. The differential diagnosis and management will be facilitated using a systematic approach. SUMMARY: The use of right ventricular pressure monitoring and the publications of guidelines for the echocardiographic assessment of right ventricular anatomy and function allow the early identification of right ventricular failure. The treatment success will be associated by optimization of the hemodynamic, echocardiographic and near-infrared spectroscopy parameters.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".