COMPARISON OF DIGITAL RADIOGRAPHY, ULTRASONOGRAPHY, AND POSITIVE CONTRAST VAGINOURETHROGRAPHY FOR DETERMINING REPRODUCTIVE STATUS OF FEMALE CATS
Bibliographic record
Abstract
It is not always possible to identify female cats that have undergone previous ovariohysterectomy based on physical examination alone. An easy, cost-effective method for screening female cats for reproductive status would be helpful for avoiding unnecessary exploratory laparotomies. The purpose of this prospective study was to compare diagnostic sensitivities of digital radiography, ultrasonography, and positive contrast vaginourethrography for determining reproductive status in female cats. Sixty-seven recently euthanized female cats of unknown medical history and reproductive status were randomly selected and included in the study. Digital abdominal radiography, digital abdominal radiography with compression, abdominal ultrasonography, and positive contrast vaginourethrography were performed in sequence by a board-certified veterinary radiologist and a second-year radiology resident. Immediately following diagnostic imaging procedures, necropsy was performed. Ultrasonography of the uterus had the highest sensitivity (86%) for determining reproductive status of all the imaging modalities tested. The specificity was 88%, and the positive predictive value and negative predictive value were 96% and 68%, respectively. The calculated sensitivities and specificities of other modalities were as follows: digital radiographs (28%, 100%), digital compression radiographs (58%, 100%), and vaginourethrography (32%, 100%). Based on McNemar's test statistic, there was a significant difference in the sensitivity of ultrasound compared to digital radiographs (P ≤ 0.05), compression radiographs (P ≤ 0.05), and vaginourethrogram (P ≤ 0.05). Findings from the current study indicated that ultrasonography is a sensitive diagnostic test for determining reproductive status in female cats. Although more readily available in private practice and shelters, digital radiography and vaginourethrography are not reliable predictors of reproductive status.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".