The Role of Tissue <scp>D</scp>oppler Imaging in Predicting Left Ventricular Filling Pressures in Patients Undergoing Cardiac Surgery: An Intraoperative Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: The perioperative management of patients undergoing cardiac surgery usually requires the accurate assessment of left ventricular filling pressures (LVFP). The gold standard for determining LVFP involves the use of pulmonary artery catheters (PAC). Using tissue Doppler indices (TDI) obtained by transthoracic echocardiography, the ratio of early transmitral filling velocity to the corresponding early mitral annular velocity (E/E') has a strong correlation with pulmonary capillary wedge pressure (PCWP). Little is known, however, on whether this relationship between E/E' and PCWP is valid intraoperatively using transesophageal echocardiography (TEE) during cardiac surgery. OBJECTIVE: The objective of our study was to determine whether TDI obtained by intraoperative TEE during cardiac surgery can accurately estimate PCWP using PAC as the gold standard. METHODS AND RESULTS: A total of 34 patients (26 males, mean age 64 ± 9 years) undergoing cardiac surgery were prospectively enrolled between 2010 and 2011 at a single tertiary care center. Conventional diastolic and tissue Doppler parameters were evaluated using intraoperative TEE with concurrent PAC monitoring before and after cardiopulmonary bypass (CPB) surgery. At both pre- and post-CPB, there was no significant correlation between lateral, septal, and mean E/E' obtained by TEE and PCWP. CONCLUSION: Intraoperative TEE was unable to accurately predict LVFP in patients undergoing cardiac surgery. PAC may continue to be the gold standard in the assessment of LVFP for this patient population.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it