Lung Ultrasound and Caval Indices to Assess Volume Status in Maintenance Hemodialysis Patients
Bibliographic record
Abstract
Background: Volume overload is common in end stage kidney disease (ESKD) and dialysis patients. Hence, the need for objective tools to detect such volume excess. Point of care ultrasound (POCUS) is a goal-directed, bedside examination to answer such a specific diagnostic question. Methods: One hundred Iraqi adult hemodialysis patients were recruited from February 1 to May 31, 2022. Primary clinical, dialysis data, and prescriptions were recorded. A nephrologist performed a POCUS examination after the last dialysis session of the week. In addition, an ultrasound examination of the chest was performed to detect B-lines and pleural effusion. Caval parameters included inferior vena cava (IVC) diameter and collapsibility index. Results: The mean age of the study group was 51.48 ± 14.6 years, with 53% males. The mean interdialytic weight gain was 2.74 ± 1.15 Kg. Lower limb edema and pleural effusion were present in 33% and 27%, respectively. Forty-seven percent of patients had >3 B-lines on lung ultrasound with a range of 12. Forty-three percent of patients had an IVC diameter of >2 cm, and 93% had <50% IVC collapsibility. In total, 97% of patients had evidence of excess volume by ultrasound criteria. IVC collapsibility index was the most prevalent sign of excess volume (93%). Patients without lower limb edema and pleural effusion had positive B-lines in 38.8% and 38.3%, an IVC diameter >2 cm in 46.2% and 38.3%, and IVC collapsibility <50% in 89.5% and 95.8% respectively. Conclusion: Iraqi maintenance hemodialysis patients are volume overloaded, which warrants proper intervention for detection and dialysis management. POCUS is a useful and easily performed technique to assess the volume status.
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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".