Veterinary echocardiographers' preferences for left atrial size assessment in cats: the BENEFIT project
Bibliographic record
Abstract
INTRODUCTION/OBJECTIVES: Veterinary echocardiographers' preferences for left atrial (LA) size assessment in cats have not been systematically investigated. The primary aim of this prospective exploratory study was to investigate echocardiographers' preferences concerning LA size assessment in cats. A secondary aim was to investigate echocardiographers' preferences for assessing LA size in subgroups based on geographic, demographic, and professional profiles. ANIMALS, MATERIALS, AND METHODS: An online survey instrument was designed, verified, and distributed globally to veterinary echocardiographers. RESULTS: A total of 655 veterinary echocardiographers from six continents and 54 countries, working in specialty practice (56%) and in general practice (38%), provided data. Linear two-dimensional (2D) technique was favored by most echocardiographers (n = 612) for LA size assessment. Most commonly, respondents combined linear 2D with subjective assessment (n = 227), while 209 used linear 2D-based methods alone. Most echocardiographers using linear 2D-based methods preferred the right parasternal short-axis view and to index the LA to the aorta (Ao). Approximately 10% of the respondents obtained LA dimensions from a right parasternal long-axis four-chamber view. Approximately one-third of echocardiographers that made linear measurements from 2D echocardiograms shared the same preferences regarding cat position, acquisition view, indexing method and time point identification for the LA measurement. The responses were comparably homogeneous across geographic location, level of training, years performing echocardiography, and type of practice. DISCUSSION/CONCLUSION: Most veterinary echocardiographers assessed LA size in cats using linear 2D echocardiography from a right parasternal short-axis view, and indexed LA to Ao. Respondents' preferences were similar over geographic, demographic, and professional backgrounds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".