CLINICAL APPLICATION OF PATLAK PLOT CT-GFR IN ANIMALS WITH UPPER URINARY TRACT DISEASE
Bibliographic record
Abstract
Glomerular filtration rate (GFR), an important parameter of renal function, is difficult to assess clinically. Serum creatinine and blood urea nitrogen measurements lack sensitivity, whereas radionuclide determination of GFR is not always available and requires postinjection patient isolation. GFR can be determined using computed tomography (CT), most commonly via Patlak plot analysis. Four adult cats, two adult dogs, and a foal underwent abdominal CT under general anesthesia for various diseases of the upper urinary tract. CT-GFR was measured with a single-slice dynamic acquisition and Patlak plot analysis. In five animals, the total CT-GFR appeared to be below normal, corresponding with mild (two animals) and moderate (two animals) increases of serum creatinine in four. In the two animals with normal or increased CT-GFR, serum creatinine was within the reference values. A significant negative logarithmic relationship was found between CT-GFR and serum creatinine values (P = 0.008; r2 = 0.75). No complications occurred during or following CT-GFR. CT examination provided clinically relevant information in 3/5 patients with possible ureteral obstruction and in 3/3 patients with suspected ureteral calculi. Single-slice dynamic CT-GFR was practical and provided clinically useful information in this small series of patients undergoing CT of the upper urinary tract. There was a significant relationship between CT-GFR and serum creatinine values, which supports the clinical potential of CT-GFR and justifies further investigation of this technique.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".