Bibliographic record
Abstract
Background: Conventional 2-dimensional ultrasonography is limited in its ability to detect ovarian microanatomy. The objective of this study was to determine if biomedical synchrotron techniques would be effective for imaging ovarian microanatomy, including ovarian follicles, corpora lutea, and the oocyte. Methods: A prospective, observational study was conducted at the Canadian Light Source to compare ovarian imaging ex vivo using propagation-based computed tomography (PB-CT) synchrotron imaging, ultrasonography, and histology. Bovine (n=4) and human (n=4) ovaries were imaged fresh or formalin-fixed. The effectiveness of Talbot grating interferometry computed tomography (TGI-CT) synchrotron imaging to image preserved bovine (n=1) and human (n=1) ovaries was evaluated at the SPring-8 synchrotron, Japan. Results: All antral follicles ≥ 2 mm and corpora lutea detected with ultrasonography were identified with PBCT. Mean follicle and luteal diameters did not differ among PB-CT, ultrasonography, and histology. The smallest follicle detected was superior with PB-CT (0.9 ± 0.4 mm) than ultrasonography (2.2 ± 0.2 mm, P<0.05). PB-CT, but not ultrasonography, allowed the detection of follicle wall cell layers (P<0.05). TGI-CT provided greater contrast for evaluating follicles, corpora lutea, and vasculature than PB-CT and ultrasonography. High contrast spherical structures resembling cumulus oocyte complexes were detected with PB-CT and TGI-CT; oocytes were only detected with TGI-CT. Conclusion: PB-CT was as effective as ultrasonography for measuring follicle and luteal diameters and superior to ultrasonography for visualizing follicles <2 mm, follicle wall cell layers, and cumulus oocyte complexes. TGI-CT appears to provide the greatest resolution for imaging ovarian anatomy compared to ultrasonography and PB-CT. Phase contrast CT Synchrotron can be used as a model for developing high resolution tools for imaging human ovaries.
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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 | 0.000 | 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) | 0.002 | 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".