Comparison of transrectal and transabdominal transducers for use in fast localized abdominal sonography of horses presenting with colic
Bibliographic record
Abstract
Abdominal ultrasonography is valuable in the diagnosis of equine colic. Fast localized abdominal sonography of horses (FLASH) enables practitioners with limited experience to perform ultrasonography in emergency settings. However, many practitioners only possess rectal format linear array transducers (RFLT). The hypotheses are: (a) A low frequency curvilinear transducer (LFCT) and RFLT will detect free abdominal fluid and abnormal small intestinal loops with similar frequency during FLASH, and (b) there will be a difference between the transducers for detection of gastric abnormalities and nephrosplenic entrapment. The objective is to compare transcutaneous abdominal ultrasonographic detection of abnormalities in horses presenting with colic using a LFCT and RFLT. Twenty-four horses requiring FLASH for investigation of colic were enrolled. Horses that were too painful to undergo transcutaneous abdominal ultrasonographic examination were excluded. A single investigator performed FLASH on all horses using a RFLT, while one of three other clinicians simultaneously performed FLASH using a LFCT. Comparison of abnormal findings between the two transducers was performed using Chi square, Fisher's exact or Wilcoxon tests. The incidence of identification of abnormal findings was similar between the two transducers for all comparisons except the visibility of the left kidney and stomach (kidney LFCT 81.25% vs. RFLT 22.92%, stomach LFCT 87.5% vs. RFLT 62.5%). While there are limitations to using a RFLT to identify nephrosplenic entrapment of the colon and detection of the stomach, it reliably detects other common abnormalities, including peritoneal effusion, lesions of the small intestine, and changes to the wall of the large colon and cecum.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".