A Study of Morphological and Haemodynamic Determinants of Testicular Echotexture Characteristics in the Ram
Bibliographic record
Abstract
The ultrasonographic image of an organ is a product of scattering and reflection of high-frequency ultrasound beams by discrete units of tissue. The number of acoustic tissue interfaces and vascularity affects the quantitative characteristics of grey-scale ultrasonographic images. This study was undertaken to examine the influences of scrotal/testicular integument and blood flow on testicular echotexture parameters in the ram. Serial ultrasonographic images were obtained during surgical castration of 7 Rideau Arcott rams aged 20-22 weeks. The first 2 sets of images were taken through the scrotum, prior to and after induction of anaesthesia. The third set was taken through the tunica vaginalis, the fourth set was obtained through the tunica albuginea, the fifth set was taken when the testicular cord and internal blood vessels were clamped, and the final set of images was recorded after allowing the blood to drain from dissected testicles (5 min). All images were then subjected to computerized image analyses and the testicles were processed for histology. The removal of the scrotal skin and tunica vaginalis both resulted in significant (P < 0.05) increments in numerical pixel values (NPVs) and pixel heterogeneity (standard deviation of pixel values) of the testicular parenchyma. There were no differences (P > 0.05) in testicular echotexture between images taken just before or after clamping the testicular cord vessels, or after draining. At all stages, NPVs were correlated (P </= 0.10) to the seminiferous tubule (ST) area and the ST lumen area, except for NPVs and the ST lumen area in images obtained through the tunica albuginea (P = 0.20). We concluded that: 1) attenuation of ultrasound waves by the scrotal skin and tunica vaginalis significantly altered testicular echotexture characteristics; 2) vascular blood flow did not affect the echotextural attributes of the rams' testes; and 3) NPVs were a good indicator of ST microstructure in situ and ex vivo.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".