Doppler Interrogation of the Femoral Vein in the Critically Ill Patient: The Fastest Potential Acoustic Window to Diagnose Right Ventricular Dysfunction?
Bibliographic record
Abstract
OBJECTIVES: To report the use of common femoral vein Doppler interrogation as a simple technique to diagnose right ventricular dysfunction. DESIGN: Case report. SETTING: Cardiac surgical ICU. PATIENTS: Postoperative cardiac surgical patients. INTERVENTIONS: Common femoral pulsed-wave and color Doppler examination associated with hepatic, portal, and renal venous Doppler measurement were obtained in both patients and before and after treatment in patient number 1. In addition, right ventricular pressure waveform examination was obtained in patient number 2. MEASUREMENTS AND MAIN RESULTS: The technique to obtain common femoral venous Doppler is described. Two cases of patients presenting with right ventricular dysfunction and fluid overload with portal and renal venous congestion in the perioperative period undergoing complex multivalvular cardiac surgery are presented. Hemodynamic waveform monitoring was performed alongside echocardiographic, hepatic, and renal venous flow Doppler assessment, and spectral Doppler profiles of the common femoral veins were examined. Those findings were useful in confirming our diagnosis and guiding our response to treatment. An algorithm was developed and tested on two additional hemodynamically unstable patients. CONCLUSIONS: Doppler examination of the common femoral vein is a simple, fast, and noninvasive technique that could be useful to rule in the presence of right ventricular dysfunction with venous congestion and help guide the management of such patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".