Specification and guideline for technical aspects and scanning parameter settings of neonatal lung ultrasound examination
Bibliographic record
Abstract
Lung ultrasound (LUS) is now widely used in the diagnosis and monitor of neonatal lung diseases. Nevertheless, in the published literatures, the LUS images may display a significant variation in technical execution, while scanning parameters may influence diagnostic accuracy. The inter- and intra-observer reliabilities of ultrasound exam have been extensively studied in general and in LUS. As expected, the reliability declines in the hands of novices when they perform the point-of-care ultrasound (POC US). Consequently, having appropriate guidelines regarding to technical aspects of neonatal LUS exam is very important especially because diagnosis is mainly based on interpretation of artifacts produced by the pleural line and the lungs. The present work aimed to create an instrument operation specification and parameter setting guidelines for neonatal LUS. Technical aspects and scanning parameter settings that allow for standardization in obtaining LUS images include (1) select a high-end equipment with high-frequency linear array transducer (12-14 MHz). (2) Choose preset suitable for lung examination or small organs. (3) Keep the probe perpendicular to the ribs or parallel to the intercostal space. (4) Set the scanning depth at 4-5 cm. (5) Set 1-2 focal zones and adjust them close to the pleural line. (6) Use fundamental frequency with speckle reduction 2-3 or similar techniques. (7) Turn off spatial compounding imaging. (8) Adjust the time-gain compensation to get uniform image from the near-to far-field.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".