The use of scrotal ultrasound in the evaluation of varicoceles: A survey study of reproductive specialists
Bibliographic record
Abstract
INTRODUCTION: Urologists use ultrasound in the male infertility workup to evaluate scrotal contents and objectively identify varicoceles if their presence is questionable on physical examination. We assessed practice patterns and diagnostic criteria of male reproductive urologists using ultrasound to evaluate varicoceles. METHODS: An anonymous online survey was sent to the Society for Male Reproduction and Urology (SMRU) members. We queried respondents about ultrasonographic criteria and ultrasound techniques employed in varicocele evaluation. Chi-squared was used to determine association between categorical variables. RESULTS: In total, 110/320 (34.4%) SMRU members responded. Sixty percent of respondents (66/110) reported performing scrotal ultrasound; 92.4 % (61/66) were attending urologists and 87.9% (58/66) completed an andrology fellowship. A total of 37.9% (25/66) performed their own ultrasound, while the remainder had ultrasound performed by an alternate practitioner. Among those performing their own ultrasound, 95.5% (21/22) measured varicocele venous diameter compared to 76% (29/38) when another practitioner performed the ultrasound. Venous diameter used to define a varicocele ranged from 2-4 mm. Although 80% (49/61) of respondents assessed retrograde flow during ultrasound, only 52.5% reported that retrograde flow was required for varicocele diagnosis. Almost all (60/61) indicated they would fix palpable varicoceles in patients with abnormal semen parameters. Fewer (42.6%, 26/61) respondents stated they would repair varicoceles found exclusively on ultrasound. CONCLUSIONS: Ultrasound is commonly employed by male reproductive urologists to diagnose varicoceles. We identified that practitioners use various ultrasonographic criteria and techniques for varicocele diagnosis. Study limitations include recall bias and high degree of specialization among respondents.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".