Influence of Probe Pressure on Ultrasound-Based Shear Wave Elastography of the Liver Using Comb-Push 2-D Technology
Bibliographic record
Abstract
It has been postulated that in the liver, applying increased probe pressure during ultrasound-based shear wave elastography (SWE) might lead to a false increase in the SWE result. We aimed to determine the influence of increased intercostal probe pressure when performing SWE of the liver. We also investigated the number of measurements required to achieve technically successful and reliable SWE examinations. This prospective, clinical study included 112 patients and 2240 SWE measurements of the liver. We applied probe pressure intercostally, to reduce the skin-to-liver capsule distance (SCD), which could stabilize the SWE signal and thus increase the number of technically successful measurements. We performed 10 measurements with maximum probe pressure and 10 with normal pressure in each patient. Thus, two analysis groups were compared for differences. Compared with normal pressure, maximum probe pressure significantly reduced the SCD (p < 0.001) and significantly increased the number of technically successful measurements from 981 to 1098, respectively (p < 0.001). The SWE results with normal and maximum probe pressure were 5.96 kPa (interquartile range: 2.41) and 5.45 kPa (interquartile range: 1.96), respectively (p < 0.001). In obese patients, a large SCD poses a diagnostic challenge for ultrasound SWE. We found that maximum intercostal probe pressure could reduce the SCD and increase the number of technically successful measurements, without falsely increasing the SWE result. Only three measurements were required to achieve technically successful and reliable SWE examinations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".