Point-of-care ultrasound in end-stage kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Following the miniaturization of ultrasound devices, point-of-care ultrasound (POCUS) has been proposed as a tool to enhance the value of physical examination in various clinical settings. The objective of this review is to describe the potential applications of POCUS in end-stage renal disease patients (ESRD). RECENT FINDINGS: With basic training, the clinician can perform pulmonary, vascular, cardiac, and abdominal POCUS at the bedside of ESRD patients. Pulmonary ultrasound can be used to quantify pulmonary congestion and for the differential diagnosis of dyspnea. Ultrasound of the inferior vena cava combined with simple cardiac ultrasound can be used to promptly investigate the mechanism of hemodynamic instability. Vascular ultrasound can be used for troubleshooting of arteriovenous fistula problems and for catheter installation. Multiple potential applications of POCUS in the ESRD population are reviewed, including areas of future research. SUMMARY: Acquiring basic skills in POCUS may improve patient care through the rapid identification of threats, improved diagnostic abilities for common symptoms, and safer procedures. The adoption of POCUS in undergraduate, internal medicine and nephrology training curriculums will likely lead to a gradual introduction of this technology in the care of ESRD patients.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".