Ovarian imaging in the mouse using ultrasound biomicroscopy (UBM): a validation study
Bibliographic record
Abstract
The mouse is a well accepted model for studies of human reproduction despite little being known about follicle dynamics in this species. Longitudinal studies of mouse folliculogenesis have been hampered by the lack of an appropriate imaging tool. Ultrasound biomicroscopy (UBM) may overcome this obstacle as it confers near-microscopic resolution through the use of high-frequency ultrasound waves. The objective of the present study was to determine whether UBM could be used to count and measure ovarian follicles and corpora lutea (CL) reliably in mice. Ovaries of 25 adult CD-1 mice were imaged using a 55-MHz transducer and then excised and processed for histology. Follicles and CL were counted and measured from digitally stored UBM cine-loops and photographed histological sections. Differences between techniques were assessed by Bland-Altman agreement analyses. Follicle counts yielded by the two techniques varied by only +/-1 follicle when follicles ranged between 300 and 499 microm. Perfect agreement among counts was evident when follicles were >500 microm. The total number of CL was accurately estimated using UBM; however, the number of 350-699 microm CL was underestimated and the number of CL>or=700 microm was overestimated. In conclusion, UBM can be used reliably to count and measure follicles in mice.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".