The Feasibility of Measuring Renal Blood Flow Using Transesophageal Echocardiography in Patients Undergoing Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: There is no reliable method to monitor renal blood flow intraoperatively. In this study, we evaluated the feasibility and reproducibility of left renal blood flow measurements using transesophageal echocardiography during cardiac surgery. METHODS: In this prospective noninterventional study, left renal blood flow was measured with transesophageal echocardiography during three time points (pre-, intra-, and postcardiopulmonary bypass) in 60 patients undergoing cardiac surgery. Sonograms from 6 subjects were interpreted by 2 blinded independent assessors at the time of acquisition and 6 mo later. Interobserver and intraobserver reproducibility were quantified by calculating variability and intraclass correlation coefficients. RESULTS: Patients with Doppler angles of >30 degrees (20 of 60 subjects) were eliminated from renal blood flow measurements. Left renal blood flow was successfully measured and analyzed in 36 of 60 (60%) subjects. Both interobserver and intraobserver variability were <10%. Interobserver and intraobserver reproducibility in left renal blood flow measurements were good to excellent (intraclass correlation coefficients 0.604-0.999). Left renal arterial luminal diameter for the pre, intra, and postcardiopulmonary bypass phases, ranged from 3.8 to 4.1 mm, renal arterial velocity from 25 to 35 cm/s, and left renal blood flow from 192 to 299 mL/min. CONCLUSION: In patients undergoing cardiac surgery, it was feasible in 60% of the subjects to measure left renal blood flow using intraoperative transesophageal echocardiography. The interobserver and intraobserver reproducibility of renal blood flow measurements was good to excellent.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".