Establishment of reference values of the caudal vena cava by fast-ultrasonography through different views in healthy dogs
Bibliographic record
Abstract
Abstract Background Clinical assessment of intravascular volume status is challenging. In humans, ultrasonographic assessment of the inferior vena cava diameter, directly or as a ratio to the aortic diameter is used to estimate intravascular volume status. Objectives To ultrasonographically obtain reference values (RV) for caudal vena cava diameter (CVCD), area (CVCa) and aortic ratios using 3 views in awake healthy dogs. Animals One hundred and twenty-six healthy adult dogs from clients, students, faculty, or staff. Methods Prospective, multicenter, observational study. Two observer pairs evaluated CVCD by a longitudinal subxiphoid view (SV), a transverse 11th-13th right hepatic intercostal view (HV), and a longitudinal right paralumbar view (PV). Inter-rater agreements were estimated using concordance correlation coefficients (CCC). For body weight (BW)-dependent variables, RVs were calculated using allometric scaling for variables with a CCC ≥ 0.7. Results The CCC was ≤0.43 for the CVC/aorta ratio at the PV and ≤0.43 in both inspiration and expiration for CVC at the SV. The RVs using allometric scaling for CVCa at the HV for inspiration, expiration, and for CVCD at the PV were 6.16 × BW0.762, 7.24 × BW0.787, 2.79 × BW0.390, respectively. Conclusions and Clinical Importance The CVCD, measured at the HV and PV in healthy awake dogs of various breeds has good inter-rater agreement suggesting these sites are reliable in measuring CVCD. Established RVs for CVCD for these sites need further comparison to results obtained in hypovolemic and hypervolemic dogs to determine their usefulness to evaluate volume status in dogs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".