Lung Ultrasound and Caval Indices to Assess Volume Status in Maintenance Hemodialysis Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it