Determination of glomerular filtration rate in anesthetized pigs by use of three-phase whole-kidney computed tomography and Patlak plot analysis
Bibliographic record
Abstract
OBJECTIVE: To develop a whole-kidney computed tomography (CT) technique that would allow 3-point Patlak plot determination of glomular filtration rate (GFR) and assess the correlation of GFR determined via CT (CT-GFR) with GFR determined via renal plasma clearance of inulin (Inu-GFR) in pigs. ANIMALS: 6 healthy anesthetized pigs. PROCEDURES: Each pig underwent 3-phase whole-kidney helical CT (arterial, early, and late parenchymal phases) before and after contrast medium administration. After contrast medium administration, corrected Hounsfield unit values were determined for each kidney and the aorta. A 3-point Patlak plot for each kidney was generated, and plasma clearance per unit volume was multiplied by renal volume to obtain whole-animal CT-GFR. Correlations of mean Inu-GFR for the left and right kidneys (and combined [total] values) with the corresponding CT-GFRs were assessed via linear regression and Bland-Altman analyses. RESULTS: Left kidney, right kidney, and total CT-GFRs were good predictors of the respective Inu-GFR values (r(2) = 92.3%, r(2) = 85.5%, and r(2) = 93.7%, respectively). For the left kidney, no significant bias between Inu-GFR and CT-GFR was detected. Right kidney and total CT-GFRs underestimated the corresponding Inu-GFRs (mean underestimation, -8.4 mL*min(1) and -12.6 mL*min(1), respectively). CONCLUSIONS AND CLINICAL RELEVANCE: Three-phase whole-kidney CT with Patlak plot analysis of GFR may underestimate right kidney and total Inu-GFRs in pigs. The Patlak plot generated may be sensitive to nonlinearity caused by temporal variation in GFR. Nonetheless, the 3-phase CT approach offers some practical advantages for simultaneous evaluation of renal morphology and measurement of GFR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.000 | 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".