Assessing the usefulness of B‐mode and colour Doppler sonography, and measurements of circulating progesterone concentrations for determining ovarian responses in superovulated ewes
Bibliographic record
Abstract
Contents The main goal of this study was to assess the usefulness of two imaging modalities, namely the B‐mode and colour Doppler sonography, and serum progesterone (P4) concentrations for determining the ovarian response in superovulated ewes. Twenty‐four sexually mature Santa Inês ewes underwent the superovulatory treatment consisting of eight injections of porcine FSH (total dose of 200 or 133 or 100 mg; n = 8 ewes/total dose) given at 12‐hr intervals and initiated 48 hr before CIDR® (Pfizer Inc., Auckland, New Zealand) removal. Six days after natural mating, the ovaries of all donor ewes were visualized and examined with transrectal ultrasonography and then with videolaparoscopy to identify and enumerate corpora lutea (CL) and luteinized unovulated follicles (LUFs). Jugular blood samples were collected just prior to ovarian examinations. The total number of CL (r = .78 and 0.83, p < .0001) and LUFs (r = .74 and 0.90, p < .0001) enumerated using the B‐mode and colour Doppler ultrasonographic technique, respectively, were correlated with that ascertained by videolaparoscopy. Circulating concentrations of P4 were related directly to the number of healthy CL (r = .73, p = .0002) and inversely to the number of prematurely regressing CL (r = −.46, p = .03), but the accuracy of predicting the number of short‐lived CL with serum P4 concentrations was very poor. The present results indicate that ultrasonographic imaging and serum P4 measurements on the day of embryo recovery are useful indicators of total/normal CL numbers and both ultrasonographic techniques can be used to quantify LUFs in superovulated ewes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.001 | 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".