Incidence of superficial abdominal organ identification is similar using high‐frequency linear (transrectal) and low‐frequency curvilinear (abdominal) transducers in clinically healthy horses: A pilot study
Bibliographic record
Abstract
Abdominal organ displacement is a potentially life-threatening condition in horses. Primary care veterinarians commonly make referral decisions based on a combination of clinical and ultrasonographic findings. However, published studies describing the effects of transducer on identifying abdominal organ locations in horses are currently lacking. The objective of this prospective, methods comparison, pilot study was to compare organ identification using a high-frequency linear (transrectal) transducer and a low-frequency curvilinear (abdominal) transducer for transcutaneous abdominal ultrasonography of healthy horses. Twelve clinically normal adult horses owned by the University of Calgary were enrolled in the study. Abdominal ultrasonography was performed by four practitioners, each randomly assigned to an alternating rotation of transrectal or abdominal transducer and left or right side of a horse. Using a Chi square test or Fisher's exact test, the frequency of identification for each organ was compared between both transducers. There was no significant difference in organ identification on the right side of the abdomen. On the left side, the stomach, liver, and kidney were less likely to be detected with the transrectal transducer. Compared with a low-frequency abdominal transducer, a high-frequency linear transrectal transducer delivers images that allow for organ identification in transcutaneous ultrasonography of the equine abdomen except for the left kidney, left liver, and stomach.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".