Can a renal nurse assess fluid status using ultrasound on the inferior vena cava? A cross‐sectional interrater study
Bibliographic record
Abstract
INTRODUCTION: Ultrasound of the inferior vena cava (IVC-US) has been used to estimate intravascular volume status and fluid removal during a hemodialysis session. Usually, renal nurses rely on other, imprecise methods to determine ultrafiltration. To date, no study has examined whether renal nurses can reliably perform ultrasound for volume assessment and for potential prevention of intradialytic hypotension. This pilot study aimed to determine if a renal nurse could master the skill of performing and correctly interpreting Point of Care Ultrasound on patients receiving hemodialysis. METHODS: After receiving theoretical training and performing 100 training scans, a renal nurse performed 60 ultrasound scans on 10 patients. These were categorized by the nurse into hypovolemic, euvolemic, or hypervolemic through measurement of the maximal diameter and degree of collapse of the IVC. Scans were subsequently assessed for adequacy and quality by two sonologists, who were blinded to each other's and the nurse's results. FINDINGS: = 0.62), when comparing the nurse to an expert sonographer. DISCUSSION: A renal nurse can reliably perform ultrasound of the IVC in hemodialysis patients, obtaining high quality scans for volume assessment of hemodialysis patients. This novel approach could be more routinely applied by other renal nurses to obtain objective measures of patient volume status in the dialysis setting.
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 | 0.043 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".