Shear wave elastography evaluation of cats with chronic kidney disease
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a major health condition in cats that can lead to poor quality of life and financial implications for therapy. Currently staging and identification of CKD is limited by diagnostic testing such as creatinine and urine-specific gravity, which do not change until late in the disease course. Other methods to evaluate CKD would be valuable in the clinical setting. Shear wave elastography is one novel ultrasound method, which has shown promise in identifying increases in tissue stiffness and identifying CKD in people. As CKD is often histologically characterized by tubulointerstitial fibrosis, shear wave elastography has the potential to identify CKD and differentiate between stages of CKD in cats. This prospective observational case-control study with 78 cats found no difference in shear wave velocities between groups (P = 0.33), a contradictory finding to one prior publication. There was no effect of weight (P = 0.65), nor the presence of mineralization (P = 0.31) or infarction (P = 0.52) on cortical shear wave velocities. There was a significant effect of age on shear wave velocity (P = 0.018) where velocities increased with age. The intraclass correlation coefficient was only moderate (0.62). Possible reasons for the difference in results between our work and that published prior, include differences in methodology and differences in instrumentation. Variability in measurements in our population may be due to the effects of respiratory motion or limitations in shear wave elastography software. As such, shear wave elastography is not currently recommended as a tool to evaluate CKD in cats and further work is necessary.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".