Comparison Between a Suprasternal or High Parasternal Approach and an Abdominal Approach for Measuring Superior Vena Cava Doppler Velocity in Neonates
Bibliographic record
Abstract
OBJECTIVES: Superior vena cava (SVC) flow is becoming an important hemodynamic measurement in neonates through its use in targeted neonatal echocardiography. Previous studies measured the flow velocity in the SVC through an abdominal approach. In adults and children, the abdominal and the suprasternal or high parasternal window are considered equivalent. We compared the two approaches in neonates. Our hypothesis was that the two echocardiographic approaches would yield similar results. METHODS: We conducted a prospective observational study of 40 neonates with gestational ages of 23 to 40 weeks and weights of 540 to 3805 g. Interventions included measurements of SVC flow velocity from an abdominal approach and a suprasternal or high parasternal approach. The main outcome measure was the SVC velocity time integral. RESULTS: The SVC velocity time integral was able to be measured from both approaches in all patients. The abdominal velocity time integral yielded on average slightly higher values by 5.1% (95% confidence interval, 0.6% to 9.8%). This finding was statistically significant for the whole sample (P = .025). The median of the absolute percent difference between measurements was 9.7% (range, 1.6% to 28.8%). The individual results were within the 95% confidence interval for intraobserver variability of the thoracic velocity time integral in 36 of 40 neonates. Times to completion were similar in both groups, with a slight advantage for the thoracic approach in larger neonates. CONCLUSIONS: The suprasternal or high parasternal approach is feasible and an acceptable alternative to the abdominal approach for measuring SVC flow velocity in the context of targeted neonatal echocardiography. Angle correction is usually necessary.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".