Acoustic radiation force imaging (ARFI) in the non-distended bladder does not predict abnormal urodynamic parameters in children
Bibliographic record
Abstract
INTRODUCTION: Acoustic radiation force imaging (ARFI) is a recently developed form of ultrasound imaging that allows in vivo measurement of tissue stiffness. This technology could be useful at predicting bladder compliance in children. We hypothesize that tissue stiffness, as measured by ARFI, correlates with abnormal bladder compliance and capacity in patients with bladder dysfunction. METHODS: Patients who presented for cystometrography (CMG) underwent ARFI of the bladder wall. Nine bladder wall shear wave speed (SWS) measurements were acquired using point and 2D ultrasound shear wave elastography. The mean for each ARFI technique was correlated to bladder compliance, calculated using Wahl's dimensionless number. ARFI parameters also were correlated with bladder capacity. RESULTS: A total of 25 patients were enrolled. Mean age at time of enrollment was 4.2±3.9 years (range two months to 15 years). There was no significant correlation between bladder compliance and point shear wave speed measurements (r=-0.22, p=0.31) or 2D shear wave speed measurements (r=-0.35, p=0.1). A total of 19 patients had bladder capacity below expected bladder capacity (EBC). There was no significant correlation between bladder capacity and point shear wave speed measurements (r =-0.08, p=0.7) or 2D shear wave speed measurements (r=-0.36, p=0.09). CONCLUSIONS: Our results did not demonstrate a significant correlation between bladder wall ARFI shear wave measurements and bladder compliance or bladder capacity. Further studies are warranted to determine whether ARFI may be used to predict abnormal urodynamic parameters in children.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".