Left atrial measurement in lateral <i>versus</i> sternal recumbency in cats undergoing focused cardiac ultrasound examination.
Bibliographic record
Abstract
Objective: To compare left atrial measurements carried out by an emergency and critical care (ECC) clinician on cats in lateral and sternal recumbency. Animals and procedures: A prospective observational study was conducted between December 2019 and January 2021 at the university teaching hospital at University of Liège. One hundred and two hospitalized cats were enrolled. Focused cardiac ultrasound (FOCUS) was performed in right lateral and sternal recumbency by a single FOCUS-trained ECC resident. Standard right parasternal long- and short-axis views were recorded. After randomization of the cineloops, the same blinded resident measured maximal left atrial dimension (LAD) and the ratio of left atrial to aortic diameter (LA:Ao). Reproducibility was assessed using the Bland-Altman method. Results: The LA:Ao and LAD measurements in lateral (LA:Ao median: 1.37, range: 1.02 to 3.22; LAD median: 13.25, range: 7.90 to 32.90) and sternal (LA:Ao median: 1.38, range: 1.06 to 3.22; LAD median: 13.00, range: 8.00 to 32.90) recumbency were not significantly different (bias: -0.003, CI -0.014, 0.007; and bias: -0.101, CI -0.231, 0.029, respectively). Conclusions and clinical relevance: The FOCUS technique was successfully applied in sternal recumbency in almost all cats. The LAD and LA:Ao measured in sternal and lateral recumbency were not significantly different. Cardiac left atrial measurements obtained using FOCUS can be reliably assessed in sternal recumbency in hospitalized, stable cats.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| 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".