Magnetic Resonance Image Attributes of the Ovarian Follicle Wall During Development and Regression1
Bibliographic record
Abstract
We analyzed image characteristics in T(1)-, T(2)-, and diffusion-weighted in vitro magnetic resonance (MR) images acquired at predefined stages of the ovarian cycle in 36 heifers to test the hypothesis that MR image attributes of the follicle wall reflect the physiologic status of ovarian follicles (viable, atretic, dominant, subordinate). Numerical pixel values (NPV), standard deviation of pixel values (heterogeneity), and area under the curve were used to assess images of follicle walls. Pixel values of the wall were used to calculate a regression line from which intercept, slope, and coefficient of determination were calculated. In T(1) images, NPV of dominant follicles were less likely to fit a regression line at the preovulatory phase than at any other phase (P < 0.1). Preovulatory dominant follicles had lower area under the curve in diffusion-weighted images than early and late static dominant follicles of the anovulatory wave (P < 0.02). Subordinate follicles in the presence of a preovulatory dominant follicle had lower mean NPV in T(1)- and T(2)-weighted images and lower intercepts in T(1)-weighted images than subordinate follicles of the anovulatory wave (P < 0.02). Early atresia of dominant follicles was identified at the late static phase by greater area, mean NPV, and slope in T(2)-weighted images (P < 0.02). Preovulatory dominant follicles had poor fit of NPV to a regression line in T(1)-weighted images and lower area under the curve in diffusion images. Atretic follicles had brighter walls with more acute transitions from follicular fluid to stroma in T(2)-weighted images and more heterogeneous walls in diffusion images. The MR image attributes of the follicle wall reflected the physiologic status of dominant and largest subordinate follicles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".