Veterinary echocardiographers' preferences for left atrial size assessment in dogs: the BENEFIT project
Bibliographic record
Abstract
INTRODUCTION/OBJECTIVES: Veterinary echocardiographers' preferences for left atrial (LA) size assessment in dogs have never been systematically investigated. The primary aim of this international survey study was to investigate echocardiographers' preferences for LA size assessment in dogs. The 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 the veterinary echocardiographers. RESULTS: A total of 670 echocardiographers from 54 countries on six continents completed the survey. Most echocardiographers (n = 621) used linear two-dimensional (2D)-based methods to assess LA size, 379 used subjective assessment, and 151 used M-mode-based methods. Most commonly, echocardiographers combined linear 2D-based methods with subjective assessment (n = 222), whereas 191 used linear 2D-based methods alone. Most echocardiographers (n = 436) using linear 2D-based methods preferred the right parasternal short-axis view and indexed the LA to the aorta. Approximately 30% (n = 191) of the echocardiographers who performed linear measurements from 2D echocardiograms shared the same preferences regarding dog position, acquisition view, indexing method, and identification of the time-point used for the measurement. The responses were comparably homogeneous across geographic location, training level, years of performing echocardiography, and type of practice. DISCUSSION/CONCLUSION: Most veterinary echocardiographers assessed LA size in dogs using linear 2D echocardiography from a right parasternal short-axis view, and by indexing the LA to the aorta. The respondents' preferences were similar across 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".