Magnetic resonance image attributes of the bovine corpus luteum during development and regression
Bibliographic record
Abstract
To determine whether magnetic resonance (MR) image attributes of the corpus luteum (CL) reflect its physiologic status at different phases of the bovine ovarian cycle, we analyzed the numerical pixel values (NPVs), relaxation rates, proton densities (PDs), and apparent diffusion coefficients (ADCs) from T(1)-, T(2)-, and diffusion-weighted in vitro images and maps of the CL acquired at defined phases of luteal function. Ovaries were removed and serum samples taken on days 3, 6, and 10, or >/=17 (day 0 = ovulation), representing metestrus (n = 10), early diestrus (n = 7), mid-diestrus (n = 9), and proestrus (n = 7), respectively. Regions of interest (ROIs) in each quadrant of the CL (which occupied at least 20% of the quadrant) were selected for analysis. Three MR image slices were analyzed: the slice with the greatest cross-sectional area of the CL, and the slices acquired immediately before and after that slice. The mean NPVs of the CL in T(1)-weighted images increased at each phase from metestrus (1,104 +/- 40 msec) to early diestrus (1,119 +/- 77 msec), to late diestrus (1,206 +/- 43 msec) and proestrus (1446 +/- 80 msec; P < 0.001). The mean NPVs in T(1)-weighted images were higher in regressing CL (proestrous) than in any other phase (P < 0.002). Grayscale heterogeneity of CL in T(1)-weighted images tended to increase during regression (P < 0.07). Regressing CL (proestrus) exhibited higher T(1)-weighted mean NPVs (P < 0.01) and tended to have greater heterogeneity (P < 0.06) than growing (metestrus) and mature (diestrus) CL, even though similar progesterone concentrations were observed. The increased brightness and heterogeneity of regressing CL in T(1)-weighted images appeared to be correlated with increased connective tissue and triglyceride content and decreased vascularity. It is anticipated that diagnostic markers for luteal viability and atresia in the in vitro bovine model will be applied to in vivo studies in women.
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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 | 0.001 |
| 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".