Single-slice dynamic computed tomographic determination of glomerular filtration rate by use of Patlak plot analysis in anesthetized pigs
Bibliographic record
Abstract
OBJECTIVE: To compare glomerular filtration rate (GFR) as estimated from Patlak plot analysis by use of single-slice computed tomography (CT) with that obtained from clearance of plasma inulin in pigs. ANIMALS: 8 healthy anesthetized juvenile pigs. PROCEDURES: All pigs underwent precontrast, whole-kidney, helical CT; postcontrast single-slice dynamic CT; and postcontrast, whole-kidney CT for volume determination. On dynamic images, corrected Hounsfield unit values were determined for each kidney and the aorta. A Patlak plot for each kidney was generated, and plasma clearance per unit volume was multiplied by renal volume to obtain whole-animal contrast clearance. Mean GFR determined via inulin clearance (Inu-GFR) was measured from each kidney and correlated to mean GFR determined via CT (CT-GFR) for the left kidney, right kidney, and both kidneys by use of linear regression and Bland-Altman analyses. RESULTS: CT-GFR results from 7 pigs were valid. Total and right kidney Inu-GFR were correlated with total and right kidney CT-GFR (total, R(2) = 0.85; right kidney, R(2) = 0.86). However, left kidney CT-GFR was poorly correlated with left kidney Inu-GFR (R(2) = 0.47). Bland-Altman analysis revealed no significant bias between Inu-GFR and CT-GFR for the left kidney, right kidney, or both kidneys. CONCLUSIONS AND CLINICAL RELEVANCE: CT-GFR as determined by use of a single-slice acquisition technique, low-dose of iohexol, and Patlak plot analysis correlated without bias with Inu-GFR for the right kidney and both kidneys (combined). This technique has promise as an accurate CT-GFR method that can be combined with renal morphologic evaluation.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".