Optical coherence tomography: a novel modality for scrotal imaging
Bibliographic record
Abstract
Background: For patients with nonobstructive azoospermia,sperm retrieval rates remain modest. We describe the use ofoptical coherence tomography to improve retrieval rates and todecrease tissue destruction.Methods: Four patients underwent diagnostic testicular biopsyand imaging with the Niris optical coherence tomography de -vice. We performed a descriptive comparison between optic alcoherence tomographic images and conventional histology.Results: The measured seminiferous tubule diameter differed by16 μm between comparative imaging from optical coherencetomography and conventional histology using hematoxylin andeosin staining.Conclusion: We illustrate the usefulness of optical coherencetomography in the setting of testicular biopsy and the managementof nonobstructive azoospermia.Contexte : Chez les patients atteints d'azoospermie non obstructive,les taux de collecte de spermatozoïdes demeurent modestes.Nous décrivons le recours à une tomographie optiquecohérente pour améliorer les taux de collecte et réduire ladestruction tissulaire.Méthodes : Quatre patients ont subi une biopsie testiculaire diagnostiqueet une épreuve d'imagerie à l'aide d'un appareil Nirisde tomographie optique cohérente. Une comparaison descriptivea été effectuée entre les images obtenues par tomographieoptique cohérente et les résultats des épreuves histologiquesstandard.Résultats : La différence dans le diamètre des tubules séminifèresmesuré par tomographie optique cohérente et par coloration histologiqueà l'hématoxyline-éosine n'était que de 16 μm.Conclusion : Nous présentons une étude descriptive illustrant l’uti -lité de la tomographie optique cohérente pendant une biopsietesticulaire en vue de la prise en charge d'une azoospermie nonobstructive.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".