Development of a modified sampling and calculation method for isotope plasma clearance assessment of the glomerular filtration rate in patients with cirrhosis and ascites
Bibliographic record
Abstract
AIM: The aim of this study was to identify a practical sampling regimen and calculation method that could be used to measure the glomerular filtration rate in patients with ascites using plasma sampling. PATIENTS AND METHODS: Thirteen potential liver transplant patients with cirrhosis and ascites were injected with Cr-51 ethylenediaminetetraacetic acid, and plasma samples were obtained at up to 16 time points for each patient. Reference clearance values were calculated using the area under the plasma clearance curve, which was calculated using all the available data points. Clearance calculations were then performed using three and four data points from each patient and three different calculation methods to identify a sampling regimen and calculation method that yielded good agreement with the reference values. RESULTS: The reference clearances ranged from 6 to 80 ml/min. Sampling at 2, 4, 8 and 24 h and calculation of the area under the plasma clearance curve using a log-linear trapezoidal rule with extrapolation to zero and infinity yielded a relative root mean square difference from the reference of less than 7%. CONCLUSION: This method for measuring glomerular filtration rate in patients with cirrhosis and ascites was found to be more accurate than the slope-intercept technique and is a practical alternative to urine collection.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".