P144Color Doppler imaging of the testis in azoospermic subjects as a predictor of spermatozoa retrieval on testicular sperm extarction
Bibliographic record
Abstract
Background There are no clinical parameters for identification of spermatogenic tissue within the testis of azoospermic subjects. Consequently, multiple biopsies are performed until spermatozoa are found. The aim of this study was to evaluate whether testicular blood flow can be a predictor of spermatozoa retrieval on TESE. Method Color Doppler ultrasound was performed in 13 azoospermic subjects affected by primary testicular pathology and in 7 fertile subjects. The PI, RI, and S/D ratio reflecting resistance to flow were measured in the mid‐portion of the testis in the longitudinal view. All azoospermic subjects underwent TESE in which a biopsy was taken from the head, mid, and tale portion of the testis. The specimens were evaluated for structure and for the presence of spermatozoa. Results Blood flow indices were similar in azoospermic subjects as compared to fertile subjects. (mean ± SD; PI = 0.99 ± 0.64, RI = 0.52 ± 0.16, S/D = 2.16 ± 0.74; PI = 0.99 ± 0.19, RI = 0.59 ± 0.07, S/D = 2.51 ± 0.48, respectively). In seven patients, spermatozoa was found in one or more of the testicular specimens. Blood flow indices in this group were similar to the indices in azoospermic patients in whom spermatozoa was not found. Conclusion At present testicular blood flow as evaluated by color Doppler can not serve as a predictor of spermatozoa retrieval on TESE.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".