Doppler Renal Resistance Index for the Prediction of Response to Passive Leg‐Raising Following Cardiac Surgery
Bibliographic record
Abstract
PURPOSE: Doppler-based renal resistance index (RI) can be measured at the bedside of critically ill patients. This study was designed to assess if the RI predicted an increase in cardiac output (CO) following passive leg-raising (PLR) in patients admitted to the intensive care unit after cardiac surgery. METHODS: During this single center prospective study, Doppler assessment of RI and measurements of CO using the thermodilution method were performed, after surgery, in the intensive care unit before and after PLR. A positive response to PLR was defined as a ≥10% increase in CO. RESULTS: We included 30 patients. The mean RI was higher before (0.694 ±0.069) than after PLR (0.679 ± 0.069) (P = .02) with a median change of -0.012 (IQR: -0.042;0.000). Following PLR, 9 patients (30%) had a >10% increase in CO. In patients with a positive PLR response, the decrease in the RI during PLR was more pronounced than in patients who did not respond to PLR (PLR ± 0.042 (IQR: -0.051; -0.040) vs PLR ± -0.008 (IQR: -0.032; 0.015) (P = .004). There was a significant negative association between RI change in response to PLR and a 10% increase in CO following PLR (OR: 1.63 (CI:1.07-2.47) (P = .02) per -0.01 change). CONCLUSION: An increase in CO following PLR was associated with a significant decrease in RI. Variations of RI in response to PLR should be further studied as a tool to predict fluid responsiveness. However, their clinical utility could be limited by the small magnitude of the variations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".