The Prevalence of Systemic Venous Congestion Post Kidney Transplant Detected by Point of Care Ultrasound (POCUS)
Bibliographic record
Abstract
Systemic venous congestion is a known cause of acute kidney injury (AKI), but its presence in kidney transplant patients has not been previously described in the literature. The objective of this study was to determine the prevalence of systemic venous congestion in recent kidney transplant recipients. We conducted a prospective, longitudinal, descriptive study including 30 adult patients during the first week post-renal transplant at the Instituto de Trasplante y Alta Complejidad in Buenos Aires, Argentina. Venous congestion was detected in 53% of patients (16/30), but only 13.3% (4/30) presented moderate to severe congestion. Pulmonary congestion was more frequent: 70% (21/30) of the patients presented some degree of pulmonary congestion, and 30% (9/30) had moderate or severe congestion. In the venous congestion group, 75% of patients developed delayed graft function (DGF) compared to 57% in the non-congestion group, although this difference was not statistically significant (p<0.3). Body weight and physical examination-two commonly used methods to guide decisions on dialysis initiation and fluid management-were found to be unreliable for assessing the true volume status. In conclusion, venous congestion was observed during the first week following renal transplantation; however, moderate to severe congestion was uncommon, affecting only 13.3% of patients. While DGF was more frequently observed in patients with congestion, a statistically significant association could not be established. Further studies with larger sample sizes are needed to better evaluate this potential relationship.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".