Sonographic Findings in Pathology of the Penis
Bibliographic record
Abstract
Introduction: In this article, we describe the sonographic appearance of the normal penis and define the features of three pathologies. Recognition of anatomy and the normal sonographic appearance serves as the baseline for sonographers to become proficient in completing a thorough evaluation. Methods: This case series will discuss three pathological processes in the penis: Peyronie disease, low-flow priapism, and an infected penile epidermoid inclusion cyst. Clinical presentations, sonographic findings, and tips on obtaining diagnostic images will be described. Results: Sonographic examination of the penis, though well established in the literature, can be a challenging exam for sonographers, given the limited exposure in most ultrasound training programs and in daily practice. We spoke with many sonographers to determine their experiences in this area of sonography in order to provide helpful advice and scanning tips. Included are normal penile anatomical appearances as well as sonographic findings in three pathologies. We will provide suggestions on how to make patients comfortable during an exam that most find compromising. Discussion: While sonography for penile pathology can be challenging, it is a valuable tool for diagnosis and treatment. This article aims to equip sonographers with a review of anatomy, physiology and pathology of the penis. This guide outlines the sonographic features of normal anatomy and three pathological conditions encountered in routine sonographic practice.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".