Femoral vein pulsatility: a simple tool for venous congestion assessment
Bibliographic record
Abstract
BACKGROUND: Femoral vein Doppler (FVD) is simpler than the VExUS score which is a multimodal scoring system based on combination of IVC diameter, hepatic venous Doppler, portal vein pulsatility and renal vein Doppler, may be useful in assessing right ventricular overload and signs of venous congestion. There is limited data on the relationship between FVD and VExUS score. RESULTS: Adult post-cardiac surgery patients were assessed for venous congestion using the VExUS score and FVD. Agreement between VExUS and FVD was studied using Kappa test, sensitivity, specificity, PPV and NPV for VExUS and FVD was calculated keeping CVP as gold standard. In total, 107 patients were enrolled, with a mean age of 55.67 ± 12.76. The accuracy of VExUS and FVD for detecting venous congestion was 80.37 (95% CI of 71.5 to 87.4) and 74.7 (95% CI of 65.4 to 82.6), respectively. The level of agreement between FVD and VExUS was moderate (Kappa value of 0.62, P < 0.001) while the agreement between FVD and CVP was weak (Kappa value of 0.49, P < 0.001). CONCLUSION: FVD has good accuracy for detecting venous congestion and shows moderate agreement with VExUS grading. With potentially easier physical accessibility and a shorter learning curve for novices, it may be a simple and valuable tool for assessing venous congestion.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".